Patent Publication Number: US-2022229805-A1

Title: Snapshot Management in a Storage System

Description:
RELATED APPLICATIONS 
     The present application is a continuation-in-part application of U.S. patent application Ser. No. 16/834,762, entitled “Unified Storage on Block Containers” and filed on Mar. 30, 2020, which is expressly incorporated by reference herein. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The accompanying drawings illustrate various embodiments and are a part of the specification. The illustrated embodiments are merely examples and do not limit the scope of the disclosure. Throughout the drawings, identical or similar reference numbers designate identical or similar elements. 
       FIG. 1A  illustrates a first example system for data storage in accordance with some implementations. 
       FIG. 1B  illustrates a second example system for data storage in accordance with some implementations. 
       FIG. 1C  illustrates a third example system for data storage in accordance with some implementations. 
       FIG. 1D  illustrates a fourth example system for data storage in accordance with some implementations. 
       FIG. 2A  is a perspective view of a storage cluster with multiple storage nodes and internal storage coupled to each storage node to provide network attached storage, in accordance with some embodiments. 
       FIG. 2B  is a block diagram showing an interconnect switch coupling multiple storage nodes in accordance with some embodiments. 
       FIG. 2C  is a multiple level block diagram, showing contents of a storage node and contents of one of the non-volatile solid state storage units in accordance with some embodiments. 
       FIG. 2D  shows a storage server environment, which uses embodiments of the storage nodes and storage units of some previous FIG.s in accordance with some embodiments. 
       FIG. 2E  is a blade hardware block diagram, showing a control plane, compute and storage planes, and authorities interacting with underlying physical resources, in accordance with some embodiments. 
       FIG. 2F  depicts elasticity software layers in blades of a storage cluster, in accordance with some embodiments. 
       FIG. 2G  depicts authorities and storage resources in blades of a storage cluster, in accordance with some embodiments. 
       FIG. 3A  sets forth a diagram of a storage system that is coupled for data communications with a cloud services provider in accordance with some embodiments of the present disclosure. 
       FIG. 3B  sets forth a diagram of a storage system in accordance with some embodiments of the present disclosure. 
       FIG. 3C  sets forth an example of a cloud-based storage system in accordance with some embodiments of the present disclosure. 
       FIG. 3D  illustrates an example of a computing device that may be specifically configured to perform one or more of the processes described herein in accordance with some embodiments of the present disclosure. 
       FIGS. 4A-4B  illustrate an example of a data storage system that includes a block container system and a container utilization system in accordance with some embodiments of the present disclosure. 
       FIG. 5  illustrates an example of a data structure resource used to represent a data instance in accordance with some embodiments. 
       FIGS. 6A-B  illustrate examples of data storage service systems configured to interface with a container utilization system in accordance with some embodiments of the present disclosure. 
       FIGS. 7-11  illustrate example methods of a data storage system in accordance with some embodiments of the present disclosure. 
       FIG. 12  illustrates an example of a data storage system that includes a snapshot management system in accordance with some embodiments of the present disclosure. 
       FIGS. 13A-13C  illustrate examples of implementations of a snapshot management system in accordance with some embodiments of the present disclosure. 
       FIGS. 14A-14D  illustrate an example of a data protection plan for a storage system in accordance with some embodiments of the present disclosure. 
       FIGS. 15A-15B  illustrate an example of another data protection plan for a storage system in accordance with some embodiments of the present disclosure. 
       FIGS. 16-21  illustrate example methods of snapshot management for a storage system in accordance with some embodiments of the present disclosure. 
    
    
     DESCRIPTION OF EMBODIMENTS 
     Example methods, apparatus, and products for data storage in accordance with embodiments of the present disclosure are described with reference to the accompanying drawings, beginning with  FIG. 1A .  FIG. 1A  illustrates an example system for data storage, in accordance with some implementations. System  100  (also referred to as “storage system” herein) includes numerous elements for purposes of illustration rather than limitation. It may be noted that system  100  may include the same, more, or fewer elements configured in the same or different manner in other implementations. 
     System  100  includes a number of computing devices  164 A-B. Computing devices (also referred to as “client devices” herein) may be embodied, for example, a server in a data center, a workstation, a personal computer, a notebook, or the like. Computing devices  164 A-B may be coupled for data communications to one or more storage arrays  102 A-B through a storage area network (‘SAN’)  158  or a local area network (‘LAN’)  160 . 
     The SAN  158  may be implemented with a variety of data communications fabrics, devices, and protocols. For example, the fabrics for SAN  158  may include Fibre Channel, Ethernet, Infiniband, Serial Attached Small Computer System Interface (‘SAS’), or the like. Data communications protocols for use with SAN  158  may include Advanced Technology Attachment (‘ATA’), Fibre Channel Protocol, Small Computer System Interface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’), HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or the like. It may be noted that SAN  158  is provided for illustration, rather than limitation. Other data communication couplings may be implemented between computing devices  164 A-B and storage arrays  102 A-B. 
     The LAN  160  may also be implemented with a variety of fabrics, devices, and protocols. For example, the fabrics for LAN  160  may include Ethernet ( 802 . 3 ), wireless ( 802 . 11 ), or the like. Data communication protocols for use in LAN  160  may include Transmission Control Protocol (‘TCP’), User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperText Transfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), Handheld Device Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’), Real Time Protocol (‘RTP’), or the like. 
     Storage arrays  102 A-B may provide persistent data storage for the computing devices  164 A-B. Storage array  102 A may be contained in a chassis (not shown), and storage array  102 B may be contained in another chassis (not shown), in implementations. Storage array  102 A and  102 B may include one or more storage array controllers  110 A-D (also referred to as “controller” herein). A storage array controller  110 A-D may be embodied as a module of automated computing machinery comprising computer hardware, computer software, or a combination of computer hardware and software. In some implementations, the storage array controllers  110 A-D may be configured to carry out various storage tasks. Storage tasks may include writing data received from the computing devices  164 A-B to storage array  102 A-B, erasing data from storage array  102 A-B, retrieving data from storage array  102 A-B and providing data to computing devices  164 A-B, monitoring and reporting of disk utilization and performance, performing redundancy operations, such as Redundant Array of Independent Drives (‘RAID’) or RAID-like data redundancy operations, compressing data, encrypting data, and so forth. 
     Storage array controller  110 A-D may be implemented in a variety of ways, including as a Field Programmable Gate Array (‘FPGA’), a Programmable Logic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’), System-on-Chip (‘SOC’), or any computing device that includes discrete components such as a processing device, central processing unit, computer memory, or various adapters. Storage array controller  110 A-D may include, for example, a data communications adapter configured to support communications via the SAN  158  or LAN  160 . In some implementations, storage array controller  110 A-D may be independently coupled to the LAN  160 . In implementations, storage array controller  110 A-D may include an I/O controller or the like that couples the storage array controller  110 A-D for data communications, through a midplane (not shown), to a persistent storage resource  170 A-B (also referred to as a “storage resource” herein). The persistent storage resource  170 A-B main include any number of storage drives  171 A-F (also referred to as “storage devices” herein) and any number of non-volatile Random Access Memory (‘NVRAM’) devices (not shown). 
     In some implementations, the NVRAM devices of a persistent storage resource  170 A-B may be configured to receive, from the storage array controller  110 A-D, data to be stored in the storage drives  171 A-F. In some examples, the data may originate from computing devices  164 A-B. In some examples, writing data to the NVRAM device may be carried out more quickly than directly writing data to the storage drive  171 A-F. In implementations, the storage array controller  110 A-D may be configured to utilize the NVRAM devices as a quickly accessible buffer for data destined to be written to the storage drives  171 A-F. Latency for write requests using NVRAM devices as a buffer may be improved relative to a system in which a storage array controller  110 A-D writes data directly to the storage drives  171 A-F. In some implementations, the NVRAM devices may be implemented with computer memory in the form of high bandwidth, low latency RAM. The NVRAM device is referred to as “non-volatile” because the NVRAM device may receive or include a unique power source that maintains the state of the RAM after main power loss to the NVRAM device. Such a power source may be a battery, one or more capacitors, or the like. In response to a power loss, the NVRAM device may be configured to write the contents of the RAM to a persistent storage, such as the storage drives  171 A-F. 
     In implementations, storage drive  171 A-F may refer to any device configured to record data persistently, where “persistently” or “persistent” refers as to a device&#39;s ability to maintain recorded data after loss of power. In some implementations, storage drive  171 A-F may correspond to non-disk storage media. For example, the storage drive  171 A-F may be one or more solid-state drives (‘SSDs’), flash memory based storage, any type of solid-state non-volatile memory, or any other type of non-mechanical storage device. In other implementations, storage drive  171 A-F may include mechanical or spinning hard disk, such as hard-disk drives (‘HDD’). 
     In some implementations, the storage array controllers  110 A-D may be configured for offloading device management responsibilities from storage drive  171 A-F in storage array  102 A-B. For example, storage array controllers  110 A-D may manage control information that may describe the state of one or more memory blocks in the storage drives  171 A-F. The control information may indicate, for example, that a particular memory block has failed and should no longer be written to, that a particular memory block contains boot code for a storage array controller  110 A-D, the number of program-erase (′P/E′) cycles that have been performed on a particular memory block, the age of data stored in a particular memory block, the type of data that is stored in a particular memory block, and so forth. In some implementations, the control information may be stored with an associated memory block as metadata. In other implementations, the control information for the storage drives  171 A-F may be stored in one or more particular memory blocks of the storage drives  171 A-F that are selected by the storage array controller  110 A-D. The selected memory blocks may be tagged with an identifier indicating that the selected memory block contains control information. The identifier may be utilized by the storage array controllers  110 A-D in conjunction with storage drives  171 A-F to quickly identify the memory blocks that contain control information. For example, the storage controllers  110 A-D may issue a command to locate memory blocks that contain control information. It may be noted that control information may be so large that parts of the control information may be stored in multiple locations, that the control information may be stored in multiple locations for purposes of redundancy, for example, or that the control information may otherwise be distributed across multiple memory blocks in the storage drive  171 A-F. 
     In implementations, storage array controllers  110 A-D may offload device management responsibilities from storage drives  171 A-F of storage array  102 A-B by retrieving, from the storage drives  171 A-F, control information describing the state of one or more memory blocks in the storage drives  171 A-F. Retrieving the control information from the storage drives  171 A-F may be carried out, for example, by the storage array controller  110 A-D querying the storage drives  171 A-F for the location of control information for a particular storage drive  171 A-F. The storage drives  171 A-F may be configured to execute instructions that enable the storage drive  171 A-F to identify the location of the control information. The instructions may be executed by a controller (not shown) associated with or otherwise located on the storage drive  171 A-F and may cause the storage drive  171 A-F to scan a portion of each memory block to identify the memory blocks that store control information for the storage drives  171 A-F. The storage drives  171 A-F may respond by sending a response message to the storage array controller  110 A-D that includes the location of control information for the storage drive  171 A-F. Responsive to receiving the response message, storage array controllers  110 A-D may issue a request to read data stored at the address associated with the location of control information for the storage drives  171 A-F. 
     In other implementations, the storage array controllers  110 A-D may further offload device management responsibilities from storage drives  171 A-F by performing, in response to receiving the control information, a storage drive management operation. A storage drive management operation may include, for example, an operation that is typically performed by the storage drive  171 A-F (e.g., the controller (not shown) associated with a particular storage drive  171 A-F). A storage drive management operation may include, for example, ensuring that data is not written to failed memory blocks within the storage drive  171 A-F, ensuring that data is written to memory blocks within the storage drive  171 A-F in such a way that adequate wear leveling is achieved, and so forth. 
     In implementations, storage array  102 A-B may implement two or more storage array controllers  110 A-D. For example, storage array  102 A may include storage array controllers  110 A and storage array controllers  110 B. At a given instance, a single storage array controller  110 A-D (e.g., storage array controller  110 A) of a storage system  100  may be designated with primary status (also referred to as “primary controller” herein), and other storage array controllers  110 A-D (e.g., storage array controller  110 A) may be designated with secondary status (also referred to as “secondary controller” herein). The primary controller may have particular rights, such as permission to alter data in persistent storage resource  170 A-B (e.g., writing data to persistent storage resource  170 A-B). At least some of the rights of the primary controller may supersede the rights of the secondary controller. For instance, the secondary controller may not have permission to alter data in persistent storage resource  170 A-B when the primary controller has the right. The status of storage array controllers  110 A-D may change. For example, storage array controller  110 A may be designated with secondary status, and storage array controller  110 B may be designated with primary status. 
     In some implementations, a primary controller, such as storage array controller  110 A, may serve as the primary controller for one or more storage arrays  102 A-B, and a second controller, such as storage array controller  110 B, may serve as the secondary controller for the one or more storage arrays  102 A-B. For example, storage array controller  110 A may be the primary controller for storage array  102 A and storage array  102 B, and storage array controller  110 B may be the secondary controller for storage array  102 A and  102 B. In some implementations, storage array controllers  110 C and  110 D (also referred to as “storage processing modules”) may neither have primary or secondary status. Storage array controllers  110 C and  110 D, implemented as storage processing modules, may act as a communication interface between the primary and secondary controllers (e.g., storage array controllers  110 A and  110 B, respectively) and storage array  102 B. For example, storage array controller  110 A of storage array  102 A may send a write request, via SAN  158 , to storage array  102 B. The write request may be received by both storage array controllers  110 C and  110 D of storage array  102 B. Storage array controllers  110 C and  110 D facilitate the communication, e.g., send the write request to the appropriate storage drive  171 A-F. It may be noted that in some implementations storage processing modules may be used to increase the number of storage drives controlled by the primary and secondary controllers. 
     In implementations, storage array controllers  110 A-D are communicatively coupled, via a midplane (not shown), to one or more storage drives  171 A-F and to one or more NVRAM devices (not shown) that are included as part of a storage array  102 A-B. The storage array controllers  110 A-D may be coupled to the midplane via one or more data communication links and the midplane may be coupled to the storage drives  171 A-F and the NVRAM devices via one or more data communications links. The data communications links described herein are collectively illustrated by data communications links  108 A-D and may include a Peripheral Component Interconnect Express (‘PCIe’) bus, for example. 
       FIG. 1B  illustrates an example system for data storage, in accordance with some implementations. Storage array controller  101  illustrated in  FIG. 1B  may similar to the storage array controllers  110 A-D described with respect to  FIG. 1A . In one example, storage array controller  101  may be similar to storage array controller  110 A or storage array controller  110 B. Storage array controller  101  includes numerous elements for purposes of illustration rather than limitation. It may be noted that storage array controller  101  may include the same, more, or fewer elements configured in the same or different manner in other implementations. It may be noted that elements of  FIG. 1A  may be included below to help illustrate features of storage array controller  101 . 
     Storage array controller  101  may include one or more processing devices  104  and random access memory (‘RAM’)  111 . Processing device  104  (or controller  101 ) represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device  104  (or controller  101 ) may be a complex instruction set computing (‘CISC’) microprocessor, reduced instruction set computing (‘RISC’) microprocessor, very long instruction word (‘VLIW’) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device  104  (or controller  101 ) may also be one or more special-purpose processing devices such as an ASIC, an FPGA, a digital signal processor (‘DSP’), network processor, or the like. 
     The processing device  104  may be connected to the RAM  111  via a data communications link  106 , which may be embodied as a high speed memory bus such as a Double-Data Rate  4  (‘DDR4’) bus. Stored in RAM  111  is an operating system  112 . In some implementations, instructions  113  are stored in RAM  111 . Instructions  113  may include computer program instructions for performing operations in in a direct-mapped flash storage system. In one embodiment, a direct-mapped flash storage system is one that that addresses data blocks within flash drives directly and without an address translation performed by the storage controllers of the flash drives. 
     In implementations, storage array controller  101  includes one or more host bus adapters  103 A-C that are coupled to the processing device  104  via a data communications link  105 A-C. In implementations, host bus adapters  103 A-C may be computer hardware that connects a host system (e.g., the storage array controller) to other network and storage arrays. In some examples, host bus adapters  103 A-C may be a Fibre Channel adapter that enables the storage array controller  101  to connect to a SAN, an Ethernet adapter that enables the storage array controller  101  to connect to a LAN, or the like. Host bus adapters  103 A-C may be coupled to the processing device  104  via a data communications link  105 A-C such as, for example, a PCIe bus. 
     In implementations, storage array controller  101  may include a host bus adapter  114  that is coupled to an expander  115 . The expander  115  may be used to attach a host system to a larger number of storage drives. The expander  115  may, for example, be a SAS expander utilized to enable the host bus adapter  114  to attach to storage drives in an implementation where the host bus adapter  114  is embodied as a SAS controller. 
     In implementations, storage array controller  101  may include a switch  116  coupled to the processing device  104  via a data communications link  109 . The switch  116  may be a computer hardware device that can create multiple endpoints out of a single endpoint, thereby enabling multiple devices to share a single endpoint. The switch  116  may, for example, be a PCIe switch that is coupled to a PCIe bus (e.g., data communications link  109 ) and presents multiple PCIe connection points to the midplane. 
     In implementations, storage array controller  101  includes a data communications link  107  for coupling the storage array controller  101  to other storage array controllers. In some examples, data communications link  107  may be a QuickPath Interconnect (QPI) interconnect. 
     A traditional storage system that uses traditional flash drives may implement a process across the flash drives that are part of the traditional storage system. For example, a higher level process of the storage system may initiate and control a process across the flash drives. However, a flash drive of the traditional storage system may include its own storage controller that also performs the process. Thus, for the traditional storage system, a higher level process (e.g., initiated by the storage system) and a lower level process (e.g., initiated by a storage controller of the storage system) may both be performed. 
     To resolve various deficiencies of a traditional storage system, operations may be performed by higher level processes and not by the lower level processes. For example, the flash storage system may include flash drives that do not include storage controllers that provide the process. Thus, the operating system of the flash storage system itself may initiate and control the process. This may be accomplished by a direct-mapped flash storage system that addresses data blocks within the flash drives directly and without an address translation performed by the storage controllers of the flash drives. 
     The operating system of the flash storage system may identify and maintain a list of allocation units across multiple flash drives of the flash storage system. The allocation units may be entire erase blocks or multiple erase blocks. The operating system may maintain a map or address range that directly maps addresses to erase blocks of the flash drives of the flash storage system. 
     Direct mapping to the erase blocks of the flash drives may be used to rewrite data and erase data. For example, the operations may be performed on one or more allocation units that include a first data and a second data where the first data is to be retained and the second data is no longer being used by the flash storage system. The operating system may initiate the process to write the first data to new locations within other allocation units and erasing the second data and marking the allocation units as being available for use for subsequent data. Thus, the process may only be performed by the higher level operating system of the flash storage system without an additional lower level process being performed by controllers of the flash drives. 
     Advantages of the process being performed only by the operating system of the flash storage system include increased reliability of the flash drives of the flash storage system as unnecessary or redundant write operations are not being performed during the process. One possible point of novelty here is the concept of initiating and controlling the process at the operating system of the flash storage system. In addition, the process can be controlled by the operating system across multiple flash drives. This is contrast to the process being performed by a storage controller of a flash drive. 
     A storage system can consist of two storage array controllers that share a set of drives for failover purposes, or it could consist of a single storage array controller that provides a storage service that utilizes multiple drives, or it could consist of a distributed network of storage array controllers each with some number of drives or some amount of Flash storage where the storage array controllers in the network collaborate to provide a complete storage service and collaborate on various aspects of a storage service including storage allocation and garbage collection. 
       FIG. 1C  illustrates a third example system  117  for data storage in accordance with some implementations. System  117  (also referred to as “storage system” herein) includes numerous elements for purposes of illustration rather than limitation. It may be noted that system  117  may include the same, more, or fewer elements configured in the same or different manner in other implementations. 
     In one embodiment, system  117  includes a dual Peripheral Component Interconnect (‘PCI’) flash storage device  118  with separately addressable fast write storage. System  117  may include a storage controller  119 . In one embodiment, storage controller  119 A-D may be a CPU, ASIC, FPGA, or any other circuitry that may implement control structures necessary according to the present disclosure. In one embodiment, system  117  includes flash memory devices (e.g., including flash memory devices  120   a - n ), operatively coupled to various channels of the storage device controller  119 . Flash memory devices  120   a - n , may be presented to the controller  119 A-D as an addressable collection of Flash pages, erase blocks, and/or control elements sufficient to allow the storage device controller  119 A-D to program and retrieve various aspects of the Flash. In one embodiment, storage device controller  119 A-D may perform operations on flash memory devices  120   a - n  including storing and retrieving data content of pages, arranging and erasing any blocks, tracking statistics related to the use and reuse of Flash memory pages, erase blocks, and cells, tracking and predicting error codes and faults within the Flash memory, controlling voltage levels associated with programming and retrieving contents of Flash cells, etc. 
     In one embodiment, system  117  may include RAM  121  to store separately addressable fast-write data. In one embodiment, RAM  121  may be one or more separate discrete devices. In another embodiment, RAM  121  may be integrated into storage device controller  119 A-D or multiple storage device controllers. The RAM  121  may be utilized for other purposes as well, such as temporary program memory for a processing device (e.g., a CPU) in the storage device controller  119 . 
     In one embodiment, system  117  may include a stored energy device  122 , such as a rechargeable battery or a capacitor. Stored energy device  122  may store energy sufficient to power the storage device controller  119 , some amount of the RAM (e.g., RAM  121 ), and some amount of Flash memory (e.g., Flash memory  120   a - 120   n ) for sufficient time to write the contents of RAM to Flash memory. In one embodiment, storage device controller  119 A-D may write the contents of RAM to Flash Memory if the storage device controller detects loss of external power. 
     In one embodiment, system  117  includes two data communications links  123   a ,  123   b . In one embodiment, data communications links  123   a ,  123   b  may be PCI interfaces. In another embodiment, data communications links  123   a ,  123   b  may be based on other communications standards (e.g., HyperTransport, InfiniBand, etc.). Data communications links  123   a ,  123   b  may be based on non-volatile memory express (‘NVMe’) or NVMe over fabrics (‘NVMf’) specifications that allow external connection to the storage device controller  119 A-D from other components in the storage system  117 . It should be noted that data communications links may be interchangeably referred to herein as PCI buses for convenience. 
     System  117  may also include an external power source (not shown), which may be provided over one or both data communications links  123   a ,  123   b , or which may be provided separately. An alternative embodiment includes a separate Flash memory (not shown) dedicated for use in storing the content of RAM  121 . The storage device controller  119 A-D may present a logical device over a PCI bus which may include an addressable fast-write logical device, or a distinct part of the logical address space of the storage device  118 , which may be presented as PCI memory or as persistent storage. In one embodiment, operations to store into the device are directed into the RAM  121 . On power failure, the storage device controller  119 A-D may write stored content associated with the addressable fast-write logical storage to Flash memory (e.g., Flash memory  120   a - n ) for long-term persistent storage. 
     In one embodiment, the logical device may include some presentation of some or all of the content of the Flash memory devices  120   a - n , where that presentation allows a storage system including a storage device  118  (e.g., storage system  117 ) to directly address Flash memory pages and directly reprogram erase blocks from storage system components that are external to the storage device through the PCI bus. The presentation may also allow one or more of the external components to control and retrieve other aspects of the Flash memory including some or all of: tracking statistics related to use and reuse of Flash memory pages, erase blocks, and cells across all the Flash memory devices; tracking and predicting error codes and faults within and across the Flash memory devices; controlling voltage levels associated with programming and retrieving contents of Flash cells; etc. 
     In one embodiment, the stored energy device  122  may be sufficient to ensure completion of in-progress operations to the Flash memory devices  120   a - 120   n  stored energy device  122  may power storage device controller  119 A-D and associated Flash memory devices (e.g.,  120   a - n ) for those operations, as well as for the storing of fast-write RAM to Flash memory. Stored energy device  122  may be used to store accumulated statistics and other parameters kept and tracked by the Flash memory devices  120   a - n  and/or the storage device controller  119 . Separate capacitors or stored energy devices (such as smaller capacitors near or embedded within the Flash memory devices themselves) may be used for some or all of the operations described herein. 
     Various schemes may be used to track and optimize the life span of the stored energy component, such as adjusting voltage levels over time, partially discharging the storage energy device  122  to measure corresponding discharge characteristics, etc. If the available energy decreases over time, the effective available capacity of the addressable fast-write storage may be decreased to ensure that it can be written safely based on the currently available stored energy. 
       FIG. 1D  illustrates a third example system  124  for data storage in accordance with some implementations. In one embodiment, system  124  includes storage controllers  125   a ,  125   b . In one embodiment, storage controllers  125   a ,  125   b  are operatively coupled to Dual PCI storage devices  119   a ,  119   b  and  119   c ,  119   d , respectively. Storage controllers  125   a ,  125   b  may be operatively coupled (e.g., via a storage network  130 ) to some number of host computers  127   a - n.    
     In one embodiment, two storage controllers (e.g.,  125   a  and  125   b ) provide storage services, such as a SCS) block storage array, a file server, an object server, a database or data analytics service, etc. The storage controllers  125   a ,  125   b  may provide services through some number of network interfaces (e.g.,  126   a - d ) to host computers  127   a - n  outside of the storage system  124 . Storage controllers  125   a ,  125   b  may provide integrated services or an application entirely within the storage system  124 , forming a converged storage and compute system. The storage controllers  125   a ,  125   b  may utilize the fast write memory within or across storage devices  119   a - d  to journal in progress operations to ensure the operations are not lost on a power failure, storage controller removal, storage controller or storage system shutdown, or some fault of one or more software or hardware components within the storage system  124 . 
     In one embodiment, controllers  125   a ,  125   b  operate as PCI masters to one or the other PCI buses  128   a ,  128   b . In another embodiment,  128   a  and  128   b  may be based on other communications standards (e.g., HyperTransport, InfiniBand, etc.). Other storage system embodiments may operate storage controllers  125   a ,  125   b  as multi-masters for both PCI buses  128   a ,  128   b . Alternately, a PCI/NVMe/NVMf switching infrastructure or fabric may connect multiple storage controllers. Some storage system embodiments may allow storage devices to communicate with each other directly rather than communicating only with storage controllers. In one embodiment, a storage device controller  119   a  may be operable under direction from a storage controller  125   a  to synthesize and transfer data to be stored into Flash memory devices from data that has been stored in RAM (e.g., RAM  121  of  FIG. 1C ). For example, a recalculated version of RAM content may be transferred after a storage controller has determined that an operation has fully committed across the storage system, or when fast-write memory on the device has reached a certain used capacity, or after a certain amount of time, to ensure improve safety of the data or to release addressable fast-write capacity for reuse. This mechanism may be used, for example, to avoid a second transfer over a bus (e.g.,  128   a ,  128   b ) from the storage controllers  125   a ,  125   b . In one embodiment, a recalculation may include compressing data, attaching indexing or other metadata, combining multiple data segments together, performing erasure code calculations, etc. 
     In one embodiment, under direction from a storage controller  125   a ,  125   b , a storage device controller  119   a ,  119   b  may be operable to calculate and transfer data to other storage devices from data stored in RAM (e.g., RAM  121  of  FIG. 1C ) without involvement of the storage controllers  125   a ,  125   b . This operation may be used to mirror data stored in one controller  125   a  to another controller  125   b , or it could be used to offload compression, data aggregation, and/or erasure coding calculations and transfers to storage devices to reduce load on storage controllers or the storage controller interface  129   a ,  129   b  to the PCI bus  128   a ,  128   b.    
     A storage device controller  119 A-D may include mechanisms for implementing high availability primitives for use by other parts of a storage system external to the Dual PCI storage device  118 . For example, reservation or exclusion primitives may be provided so that, in a storage system with two storage controllers providing a highly available storage service, one storage controller may prevent the other storage controller from accessing or continuing to access the storage device. This could be used, for example, in cases where one controller detects that the other controller is not functioning properly or where the interconnect between the two storage controllers may itself not be functioning properly. 
     In one embodiment, a storage system for use with Dual PCI direct mapped storage devices with separately addressable fast write storage includes systems that manage erase blocks or groups of erase blocks as allocation units for storing data on behalf of the storage service, or for storing metadata (e.g., indexes, logs, etc.) associated with the storage service, or for proper management of the storage system itself. Flash pages, which may be a few kilobytes in size, may be written as data arrives or as the storage system is to persist data for long intervals of time (e.g., above a defined threshold of time). To commit data more quickly, or to reduce the number of writes to the Flash memory devices, the storage controllers may first write data into the separately addressable fast write storage on one more storage devices. 
     In one embodiment, the storage controllers  125   a ,  125   b  may initiate the use of erase blocks within and across storage devices (e.g.,  118 ) in accordance with an age and expected remaining lifespan of the storage devices, or based on other statistics. The storage controllers  125   a ,  125   b  may initiate garbage collection and data migration data between storage devices in accordance with pages that are no longer needed as well as to manage Flash page and erase block lifespans and to manage overall system performance. 
     In one embodiment, the storage system  124  may utilize mirroring and/or erasure coding schemes as part of storing data into addressable fast write storage and/or as part of writing data into allocation units associated with erase blocks. Erasure codes may be used across storage devices, as well as within erase blocks or allocation units, or within and across Flash memory devices on a single storage device, to provide redundancy against single or multiple storage device failures or to protect against internal corruptions of Flash memory pages resulting from Flash memory operations or from degradation of Flash memory cells. Mirroring and erasure coding at various levels may be used to recover from multiple types of failures that occur separately or in combination. 
     The embodiments depicted with reference to  FIGS. 2A-G  illustrate a storage cluster that stores user data, such as user data originating from one or more user or client systems or other sources external to the storage cluster. The storage cluster distributes user data across storage nodes housed within a chassis, or across multiple chassis, using erasure coding and redundant copies of metadata. Erasure coding refers to a method of data protection or reconstruction in which data is stored across a set of different locations, such as disks, storage nodes or geographic locations. Flash memory is one type of solid-state memory that may be integrated with the embodiments, although the embodiments may be extended to other types of solid-state memory or other storage medium, including non-solid state memory. Control of storage locations and workloads are distributed across the storage locations in a clustered peer-to-peer system. Tasks such as mediating communications between the various storage nodes, detecting when a storage node has become unavailable, and balancing I/Os (inputs and outputs) across the various storage nodes, are all handled on a distributed basis. Data is laid out or distributed across multiple storage nodes in data fragments or stripes that support data recovery in some embodiments. Ownership of data can be reassigned within a cluster, independent of input and output patterns. This architecture described in more detail below allows a storage node in the cluster to fail, with the system remaining operational, since the data can be reconstructed from other storage nodes and thus remain available for input and output operations. In various embodiments, a storage node may be referred to as a cluster node, a blade, or a server. 
     The storage cluster may be contained within a chassis, i.e., an enclosure housing one or more storage nodes. A mechanism to provide power to each storage node, such as a power distribution bus, and a communication mechanism, such as a communication bus that enables communication between the storage nodes are included within the chassis. The storage cluster can run as an independent system in one location according to some embodiments. In one embodiment, a chassis contains at least two instances of both the power distribution and the communication bus which may be enabled or disabled independently. The internal communication bus may be an Ethernet bus, however, other technologies such as PCIe, InfiniBand, and others, are equally suitable. The chassis provides a port for an external communication bus for enabling communication between multiple chassis, directly or through a switch, and with client systems. The external communication may use a technology such as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments, the external communication bus uses different communication bus technologies for inter-chassis and client communication. If a switch is deployed within or between chassis, the switch may act as a translation between multiple protocols or technologies. When multiple chassis are connected to define a storage cluster, the storage cluster may be accessed by a client using either proprietary interfaces or standard interfaces such as network file system (‘NFS’), common internet file system (‘CIFS’), small computer system interface (‘SCSI’) or hypertext transfer protocol (‘HTTP’). Translation from the client protocol may occur at the switch, chassis external communication bus or within each storage node. In some embodiments, multiple chassis may be coupled or connected to each other through an aggregator switch. A portion and/or all of the coupled or connected chassis may be designated as a storage cluster. As discussed above, each chassis can have multiple blades, each blade has a media access control (‘MAC’) address, but the storage cluster is presented to an external network as having a single cluster IP address and a single MAC address in some embodiments. 
     Each storage node may be one or more storage servers and each storage server is connected to one or more non-volatile solid state memory units, which may be referred to as storage units or storage devices. One embodiment includes a single storage server in each storage node and between one to eight non-volatile solid state memory units, however this one example is not meant to be limiting. The storage server may include a processor, DRAM and interfaces for the internal communication bus and power distribution for each of the power buses. Inside the storage node, the interfaces and storage unit share a communication bus, e.g., PCI Express, in some embodiments. The non-volatile solid state memory units may directly access the internal communication bus interface through a storage node communication bus, or request the storage node to access the bus interface. The non-volatile solid state memory unit contains an embedded CPU, solid state storage controller, and a quantity of solid state mass storage, e.g., between 2-32 terabytes (‘TB’) in some embodiments. An embedded volatile storage medium, such as DRAM, and an energy reserve apparatus are included in the non-volatile solid state memory unit. In some embodiments, the energy reserve apparatus is a capacitor, super-capacitor, or battery that enables transferring a subset of DRAM contents to a stable storage medium in the case of power loss. In some embodiments, the non-volatile solid state memory unit is constructed with a storage class memory, such as phase change or magnetoresistive random access memory (‘MRAM’) that substitutes for DRAM and enables a reduced power hold-up apparatus. 
     One of many features of the storage nodes and non-volatile solid state storage is the ability to proactively rebuild data in a storage cluster. The storage nodes and non-volatile solid state storage can determine when a storage node or non-volatile solid state storage in the storage cluster is unreachable, independent of whether there is an attempt to read data involving that storage node or non-volatile solid state storage. The storage nodes and non-volatile solid state storage then cooperate to recover and rebuild the data in at least partially new locations. This constitutes a proactive rebuild, in that the system rebuilds data without waiting until the data is needed for a read access initiated from a client system employing the storage cluster. These and further details of the storage memory and operation thereof are discussed below. 
       FIG. 2A  is a perspective view of a storage cluster  161 , with multiple storage nodes  150  and internal solid-state memory coupled to each storage node to provide network attached storage or storage area network, in accordance with some embodiments. A network attached storage, storage area network, or a storage cluster, or other storage memory, could include one or more storage clusters  161 , each having one or more storage nodes  150 , in a flexible and reconfigurable arrangement of both the physical components and the amount of storage memory provided thereby. The storage cluster  161  is designed to fit in a rack, and one or more racks can be set up and populated as desired for the storage memory. The storage cluster  161  has a chassis  138  having multiple slots  142 . It should be appreciated that chassis  138  may be referred to as a housing, enclosure, or rack unit. In one embodiment, the chassis  138  has fourteen slots  142 , although other numbers of slots are readily devised. For example, some embodiments have four slots, eight slots, sixteen slots, thirty-two slots, or other suitable number of slots. Each slot  142  can accommodate one storage node  150  in some embodiments. Chassis  138  includes flaps  148  that can be utilized to mount the chassis  138  on a rack. Fans  144  provide air circulation for cooling of the storage nodes  150  and components thereof, although other cooling components could be used, or an embodiment could be devised without cooling components. A switch fabric  146  couples storage nodes  150  within chassis  138  together and to a network for communication to the memory. In an embodiment depicted in herein, the slots  142  to the left of the switch fabric  146  and fans  144  are shown occupied by storage nodes  150 , while the slots  142  to the right of the switch fabric  146  and fans  144  are empty and available for insertion of storage node  150  for illustrative purposes. This configuration is one example, and one or more storage nodes  150  could occupy the slots  142  in various further arrangements. The storage node arrangements need not be sequential or adjacent in some embodiments. Storage nodes  150  are hot pluggable, meaning that a storage node  150  can be inserted into a slot  142  in the chassis  138 , or removed from a slot  142 , without stopping or powering down the system. Upon insertion or removal of storage node  150  from slot  142 , the system automatically reconfigures in order to recognize and adapt to the change. Reconfiguration, in some embodiments, includes restoring redundancy and/or rebalancing data or load. 
     Each storage node  150  can have multiple components. In the embodiment shown here, the storage node  150  includes a printed circuit board  159  populated by a CPU  156 , i.e., processor, a memory  154  coupled to the CPU  156 , and a non-volatile solid state storage  152  coupled to the CPU  156 , although other mountings and/or components could be used in further embodiments. The memory  154  has instructions which are executed by the CPU  156  and/or data operated on by the CPU  156 . As further explained below, the non-volatile solid state storage  152  includes flash or, in further embodiments, other types of solid-state memory. 
     Referring to  FIG. 2A , storage cluster  161  is scalable, meaning that storage capacity with non-uniform storage sizes is readily added, as described above. One or more storage nodes  150  can be plugged into or removed from each chassis and the storage cluster self-configures in some embodiments. Plug-in storage nodes  150 , whether installed in a chassis as delivered or later added, can have different sizes. For example, in one embodiment a storage node  150  can have any multiple of 4 TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, a storage node  150  could have any multiple of other storage amounts or capacities. Storage capacity of each storage node  150  is broadcast, and influences decisions of how to stripe the data. For maximum storage efficiency, an embodiment can self-configure as wide as possible in the stripe, subject to a predetermined requirement of continued operation with loss of up to one, or up to two, non-volatile solid state storage units  152  or storage nodes  150  within the chassis. 
       FIG. 2B  is a block diagram showing a communications interconnect  173  and power distribution bus  172  coupling multiple storage nodes  150 . Referring back to  FIG. 2A , the communications interconnect  173  can be included in or implemented with the switch fabric  146  in some embodiments. Where multiple storage clusters  161  occupy a rack, the communications interconnect  173  can be included in or implemented with a top of rack switch, in some embodiments. As illustrated in  FIG. 2B , storage cluster  161  is enclosed within a single chassis  138 . External port  176  is coupled to storage nodes  150  through communications interconnect  173 , while external port  174  is coupled directly to a storage node. External power port  178  is coupled to power distribution bus  172 . Storage nodes  150  may include varying amounts and differing capacities of non-volatile solid state storage  152  as described with reference to  FIG. 2A . In addition, one or more storage nodes  150  may be a compute only storage node as illustrated in  FIG. 2B . Authorities  168  are implemented on the non-volatile solid state storages  152 , for example as lists or other data structures stored in memory. In some embodiments the authorities are stored within the non-volatile solid state storage  152  and supported by software executing on a controller or other processor of the non-volatile solid state storage  152 . In a further embodiment, authorities  168  are implemented on the storage nodes  150 , for example as lists or other data structures stored in the memory  154  and supported by software executing on the CPU  156  of the storage node  150 . Authorities  168  control how and where data is stored in the non-volatile solid state storages  152  in some embodiments. This control assists in determining which type of erasure coding scheme is applied to the data, and which storage nodes  150  have which portions of the data. Each authority  168  may be assigned to a non-volatile solid state storage  152 . Each authority may control a range of inode numbers, segment numbers, or other data identifiers which are assigned to data by a file system, by the storage nodes  150 , or by the non-volatile solid state storage  152 , in various embodiments. 
     Every piece of data, and every piece of metadata, has redundancy in the system in some embodiments. In addition, every piece of data and every piece of metadata has an owner, which may be referred to as an authority. If that authority is unreachable, for example through failure of a storage node, there is a plan of succession for how to find that data or that metadata. In various embodiments, there are redundant copies of authorities  168 . Authorities  168  have a relationship to storage nodes  150  and non-volatile solid state storage  152  in some embodiments. Each authority  168 , covering a range of data segment numbers or other identifiers of the data, may be assigned to a specific non-volatile solid state storage  152 . In some embodiments the authorities  168  for all of such ranges are distributed over the non-volatile solid state storages  152  of a storage cluster. Each storage node  150  has a network port that provides access to the non-volatile solid state storage(s)  152  of that storage node  150 . Data can be stored in a segment, which is associated with a segment number and that segment number is an indirection for a configuration of a RAID (redundant array of independent disks) stripe in some embodiments. The assignment and use of the authorities  168  thus establishes an indirection to data. Indirection may be referred to as the ability to reference data indirectly, in this case via an authority  168 , in accordance with some embodiments. A segment identifies a set of non-volatile solid state storage  152  and a local identifier into the set of non-volatile solid state storage  152  that may contain data. In some embodiments, the local identifier is an offset into the device and may be reused sequentially by multiple segments. In other embodiments the local identifier is unique for a specific segment and never reused. The offsets in the non-volatile solid state storage  152  are applied to locating data for writing to or reading from the non-volatile solid state storage  152  (in the form of a RAID stripe). Data is striped across multiple units of non-volatile solid state storage  152 , which may include or be different from the non-volatile solid state storage  152  having the authority  168  for a particular data segment. 
     If there is a change in where a particular segment of data is located, e.g., during a data move or a data reconstruction, the authority  168  for that data segment should be consulted, at that non-volatile solid state storage  152  or storage node  150  having that authority  168 . In order to locate a particular piece of data, embodiments calculate a hash value for a data segment or apply an inode number or a data segment number. The output of this operation points to a non-volatile solid state storage  152  having the authority  168  for that particular piece of data. In some embodiments there are two stages to this operation. The first stage maps an entity identifier (ID), e.g., a segment number, inode number, or directory number to an authority identifier. This mapping may include a calculation such as a hash or a bit mask. The second stage is mapping the authority identifier to a particular non-volatile solid state storage  152 , which may be done through an explicit mapping. The operation is repeatable, so that when the calculation is performed, the result of the calculation repeatably and reliably points to a particular non-volatile solid state storage  152  having that authority  168 . The operation may include the set of reachable storage nodes as input. If the set of reachable non-volatile solid state storage units changes the optimal set changes. In some embodiments, the persisted value is the current assignment (which is always true) and the calculated value is the target assignment the cluster will attempt to reconfigure towards. This calculation may be used to determine the optimal non-volatile solid state storage  152  for an authority in the presence of a set of non-volatile solid state storage  152  that are reachable and constitute the same cluster. The calculation also determines an ordered set of peer non-volatile solid state storage  152  that will also record the authority to non-volatile solid state storage mapping so that the authority may be determined even if the assigned non-volatile solid state storage is unreachable. A duplicate or substitute authority  168  may be consulted if a specific authority  168  is unavailable in some embodiments. 
     With reference to  FIGS. 2A and 2B , two of the many tasks of the CPU  156  on a storage node  150  are to break up write data, and reassemble read data. When the system has determined that data is to be written, the authority  168  for that data is located as above. When the segment ID for data is already determined the request to write is forwarded to the non-volatile solid state storage  152  currently determined to be the host of the authority  168  determined from the segment. The host CPU  156  of the storage node  150 , on which the non-volatile solid state storage  152  and corresponding authority  168  reside, then breaks up or shards the data and transmits the data out to various non-volatile solid state storage  152 . The transmitted data is written as a data stripe in accordance with an erasure coding scheme. In some embodiments, data is requested to be pulled, and in other embodiments, data is pushed. In reverse, when data is read, the authority  168  for the segment ID containing the data is located as described above. The host CPU  156  of the storage node  150  on which the non-volatile solid state storage  152  and corresponding authority  168  reside requests the data from the non-volatile solid state storage and corresponding storage nodes pointed to by the authority. In some embodiments the data is read from flash storage as a data stripe. The host CPU  156  of storage node  150  then reassembles the read data, correcting any errors (if present) according to the appropriate erasure coding scheme, and forwards the reassembled data to the network. In further embodiments, some or all of these tasks can be handled in the non-volatile solid state storage  152 . In some embodiments, the segment host requests the data be sent to storage node  150  by requesting pages from storage and then sending the data to the storage node making the original request. 
     In some systems, for example in UNIX-style file systems, data is handled with an index node or inode, which specifies a data structure that represents an object in a file system. The object could be a file or a directory, for example. Metadata may accompany the object, as attributes such as permission data and a creation timestamp, among other attributes. A segment number could be assigned to all or a portion of such an object in a file system. In other systems, data segments are handled with a segment number assigned elsewhere. For purposes of discussion, the unit of distribution is an entity, and an entity can be a file, a directory or a segment. That is, entities are units of data or metadata stored by a storage system. Entities are grouped into sets called authorities. Each authority has an authority owner, which is a storage node that has the exclusive right to update the entities in the authority. In other words, a storage node contains the authority, and that the authority, in turn, contains entities. 
     A segment is a logical container of data in accordance with some embodiments. A segment is an address space between medium address space and physical flash locations, i.e., the data segment number, are in this address space. Segments may also contain meta-data, which enable data redundancy to be restored (rewritten to different flash locations or devices) without the involvement of higher level software. In one embodiment, an internal format of a segment contains client data and medium mappings to determine the position of that data. Each data segment is protected, e.g., from memory and other failures, by breaking the segment into a number of data and parity shards, where applicable. The data and parity shards are distributed, i.e., striped, across non-volatile solid state storage  152  coupled to the host CPUs  156  (See  FIGS. 2E and 2G ) in accordance with an erasure coding scheme. Usage of the term segments refers to the container and its place in the address space of segments in some embodiments. Usage of the term stripe refers to the same set of shards as a segment and includes how the shards are distributed along with redundancy or parity information in accordance with some embodiments. 
     A series of address-space transformations takes place across an entire storage system. At the top are the directory entries (file names) which link to an inode. Inodes point into medium address space, where data is logically stored. Medium addresses may be mapped through a series of indirect mediums to spread the load of large files, or implement data services like deduplication or snapshots. Medium addresses may be mapped through a series of indirect mediums to spread the load of large files, or implement data services like deduplication or snapshots. Segment addresses are then translated into physical flash locations. Physical flash locations have an address range bounded by the amount of flash in the system in accordance with some embodiments. Medium addresses and segment addresses are logical containers, and in some embodiments use a 128 bit or larger identifier so as to be practically infinite, with a likelihood of reuse calculated as longer than the expected life of the system. Addresses from logical containers are allocated in a hierarchical fashion in some embodiments. Initially, each non-volatile solid state storage unit  152  may be assigned a range of address space. Within this assigned range, the non-volatile solid state storage  152  is able to allocate addresses without synchronization with other non-volatile solid state storage  152 . 
     Data and metadata is stored by a set of underlying storage layouts that are optimized for varying workload patterns and storage devices. These layouts incorporate multiple redundancy schemes, compression formats and index algorithms. Some of these layouts store information about authorities and authority masters, while others store file metadata and file data. The redundancy schemes include error correction codes that tolerate corrupted bits within a single storage device (such as a NAND flash chip), erasure codes that tolerate the failure of multiple storage nodes, and replication schemes that tolerate data center or regional failures. In some embodiments, low density parity check (‘LDPC’) code is used within a single storage unit. Reed-Solomon encoding is used within a storage cluster, and mirroring is used within a storage grid in some embodiments. Metadata may be stored using an ordered log structured index (such as a Log Structured Merge Tree), and large data may not be stored in a log structured layout. 
     In order to maintain consistency across multiple copies of an entity, the storage nodes agree implicitly on two things through calculations: (1) the authority that contains the entity, and (2) the storage node that contains the authority. The assignment of entities to authorities can be done by pseudo randomly assigning entities to authorities, by splitting entities into ranges based upon an externally produced key, or by placing a single entity into each authority. Examples of pseudorandom schemes are linear hashing and the Replication Under Scalable Hashing (‘RUSH’) family of hashes, including Controlled Replication Under Scalable Hashing (‘CRUSH’). In some embodiments, pseudorandom assignment is utilized only for assigning authorities to nodes because the set of nodes can change. The set of authorities cannot change so any subjective function may be applied in these embodiments. Some placement schemes automatically place authorities on storage nodes, while other placement schemes rely on an explicit mapping of authorities to storage nodes. In some embodiments, a pseudorandom scheme is utilized to map from each authority to a set of candidate authority owners. A pseudorandom data distribution function related to CRUSH may assign authorities to storage nodes and create a list of where the authorities are assigned. Each storage node has a copy of the pseudorandom data distribution function, and can arrive at the same calculation for distributing, and later finding or locating an authority. Each of the pseudorandom schemes requires the reachable set of storage nodes as input in some embodiments in order to conclude the same target nodes. Once an entity has been placed in an authority, the entity may be stored on physical devices so that no expected failure will lead to unexpected data loss. In some embodiments, rebalancing algorithms attempt to store the copies of all entities within an authority in the same layout and on the same set of machines. 
     Examples of expected failures include device failures, stolen machines, datacenter fires, and regional disasters, such as nuclear or geological events. Different failures lead to different levels of acceptable data loss. In some embodiments, a stolen storage node impacts neither the security nor the reliability of the system, while depending on system configuration, a regional event could lead to no loss of data, a few seconds or minutes of lost updates, or even complete data loss. 
     In the embodiments, the placement of data for storage redundancy is independent of the placement of authorities for data consistency. In some embodiments, storage nodes that contain authorities do not contain any persistent storage. Instead, the storage nodes are connected to non-volatile solid state storage units that do not contain authorities. The communications interconnect between storage nodes and non-volatile solid state storage units consists of multiple communication technologies and has non-uniform performance and fault tolerance characteristics. In some embodiments, as mentioned above, non-volatile solid state storage units are connected to storage nodes via PCI express, storage nodes are connected together within a single chassis using Ethernet backplane, and chassis are connected together to form a storage cluster. Storage clusters are connected to clients using Ethernet or fiber channel in some embodiments. If multiple storage clusters are configured into a storage grid, the multiple storage clusters are connected using the Internet or other long-distance networking links, such as a “metro scale” link or private link that does not traverse the internet. 
     Authority owners have the exclusive right to modify entities, to migrate entities from one non-volatile solid state storage unit to another non-volatile solid state storage unit, and to add and remove copies of entities. This allows for maintaining the redundancy of the underlying data. When an authority owner fails, is going to be decommissioned, or is overloaded, the authority is transferred to a new storage node. Transient failures make it non-trivial to ensure that all non-faulty machines agree upon the new authority location. The ambiguity that arises due to transient failures can be achieved automatically by a consensus protocol such as Paxos, hot-warm failover schemes, via manual intervention by a remote system administrator, or by a local hardware administrator (such as by physically removing the failed machine from the cluster, or pressing a button on the failed machine). In some embodiments, a consensus protocol is used, and failover is automatic. If too many failures or replication events occur in too short a time period, the system goes into a self-preservation mode and halts replication and data movement activities until an administrator intervenes in accordance with some embodiments. 
     As authorities are transferred between storage nodes and authority owners update entities in their authorities, the system transfers messages between the storage nodes and non-volatile solid state storage units. With regard to persistent messages, messages that have different purposes are of different types. Depending on the type of the message, the system maintains different ordering and durability guarantees. As the persistent messages are being processed, the messages are temporarily stored in multiple durable and non-durable storage hardware technologies. In some embodiments, messages are stored in RAM, NVRAM and on NAND flash devices, and a variety of protocols are used in order to make efficient use of each storage medium. Latency-sensitive client requests may be persisted in replicated NVRAM, and then later NAND, while background rebalancing operations are persisted directly to NAND. 
     Persistent messages are persistently stored prior to being transmitted. This allows the system to continue to serve client requests despite failures and component replacement. Although many hardware components contain unique identifiers that are visible to system administrators, manufacturer, hardware supply chain and ongoing monitoring quality control infrastructure, applications running on top of the infrastructure address virtualize addresses. These virtualized addresses do not change over the lifetime of the storage system, regardless of component failures and replacements. This allows each component of the storage system to be replaced over time without reconfiguration or disruptions of client request processing, i.e., the system supports non-disruptive upgrades. 
     In some embodiments, the virtualized addresses are stored with sufficient redundancy. A continuous monitoring system correlates hardware and software status and the hardware identifiers. This allows detection and prediction of failures due to faulty components and manufacturing details. The monitoring system also enables the proactive transfer of authorities and entities away from impacted devices before failure occurs by removing the component from the critical path in some embodiments. 
       FIG. 2C  is a multiple level block diagram, showing contents of a storage node  150  and contents of a non-volatile solid state storage  152  of the storage node  150 . Data is communicated to and from the storage node  150  by a network interface controller (‘NIC’)  202  in some embodiments. Each storage node  150  has a CPU  156 , and one or more non-volatile solid state storage  152 , as discussed above. Moving down one level in  FIG. 2C , each non-volatile solid state storage  152  has a relatively fast non-volatile solid state memory, such as nonvolatile random access memory (‘NVRAM’)  204 , and flash memory  206 . In some embodiments, NVRAM  204  may be a component that does not require program/erase cycles (DRAM, MRAM, PCM), and can be a memory that can support being written vastly more often than the memory is read from. Moving down another level in  FIG. 2C , the NVRAM  204  is implemented in one embodiment as high speed volatile memory, such as dynamic random access memory (DRAM)  216 , backed up by energy reserve  218 . Energy reserve  218  provides sufficient electrical power to keep the DRAM  216  powered long enough for contents to be transferred to the flash memory  206  in the event of power failure. In some embodiments, energy reserve  218  is a capacitor, super-capacitor, battery, or other device, that supplies a suitable supply of energy sufficient to enable the transfer of the contents of DRAM  216  to a stable storage medium in the case of power loss. The flash memory  206  is implemented as multiple flash dies  222 , which may be referred to as packages of flash dies  222  or an array of flash dies  222 . It should be appreciated that the flash dies  222  could be packaged in any number of ways, with a single die per package, multiple dies per package (i.e. multichip packages), in hybrid packages, as bare dies on a printed circuit board or other substrate, as encapsulated dies, etc. In the embodiment shown, the non-volatile solid state storage  152  has a controller  212  or other processor, and an input output (I/O) port  210  coupled to the controller  212 . I/O port  210  is coupled to the CPU  156  and/or the network interface controller  202  of the flash storage node  150 . Flash input output (I/O) port  220  is coupled to the flash dies  222 , and a direct memory access unit (DMA)  214  is coupled to the controller  212 , the DRAM  216  and the flash dies  222 . In the embodiment shown, the I/O port  210 , controller  212 , DMA unit  214  and flash I/O port  220  are implemented on a programmable logic device (‘PLD’)  208 , e.g., an FPGA. In this embodiment, each flash die  222  has pages, organized as sixteen kB (kilobyte) pages  224 , and a register  226  through which data can be written to or read from the flash die  222 . In further embodiments, other types of solid-state memory are used in place of, or in addition to flash memory illustrated within flash die  222 . 
     Storage clusters  161 , in various embodiments as disclosed herein, can be contrasted with storage arrays in general. The storage nodes  150  are part of a collection that creates the storage cluster  161 . Each storage node  150  owns a slice of data and computing required to provide the data. Multiple storage nodes  150  cooperate to store and retrieve the data. Storage memory or storage devices, as used in storage arrays in general, are less involved with processing and manipulating the data. Storage memory or storage devices in a storage array receive commands to read, write, or erase data. The storage memory or storage devices in a storage array are not aware of a larger system in which they are embedded, or what the data means. Storage memory or storage devices in storage arrays can include various types of storage memory, such as RAM, solid state drives, hard disk drives, etc. The storage units  152  described herein have multiple interfaces active simultaneously and serving multiple purposes. In some embodiments, some of the functionality of a storage node  150  is shifted into a storage unit  152 , transforming the storage unit  152  into a combination of storage unit  152  and storage node  150 . Placing computing (relative to storage data) into the storage unit  152  places this computing closer to the data itself. The various system embodiments have a hierarchy of storage node layers with different capabilities. By contrast, in a storage array, a controller owns and knows everything about all of the data that the controller manages in a shelf or storage devices. In a storage cluster  161 , as described herein, multiple controllers in multiple storage units  152  and/or storage nodes  150  cooperate in various ways (e.g., for erasure coding, data sharding, metadata communication and redundancy, storage capacity expansion or contraction, data recovery, and so on). 
       FIG. 2D  shows a storage server environment, which uses embodiments of the storage nodes  150  and storage units  152  of  FIGS. 2A-C . In this version, each storage unit  152  has a processor such as controller  212  (see  FIG. 2C ), an FPGA, flash memory  206 , and NVRAM  204  (which is super-capacitor backed DRAM  216 , see  FIGS. 2B and 2C ) on a PCIe (peripheral component interconnect express) board in a chassis  138  (see  FIG. 2A ). The storage unit  152  may be implemented as a single board containing storage, and may be the largest tolerable failure domain inside the chassis. In some embodiments, up to two storage units  152  may fail and the device will continue with no data loss. 
     The physical storage is divided into named regions based on application usage in some embodiments. The NVRAM  204  is a contiguous block of reserved memory in the storage unit  152  DRAM  216 , and is backed by NAND flash. NVRAM  204  is logically divided into multiple memory regions written for two as spool (e.g., spool_region). Space within the NVRAM  204  spools is managed by each authority  168  independently. Each device provides an amount of storage space to each authority  168 . That authority  168  further manages lifetimes and allocations within that space. Examples of a spool include distributed transactions or notions. When the primary power to a storage unit  152  fails, onboard super-capacitors provide a short duration of power hold up. During this holdup interval, the contents of the NVRAM  204  are flushed to flash memory  206 . On the next power-on, the contents of the NVRAM  204  are recovered from the flash memory  206 . 
     As for the storage unit controller, the responsibility of the logical “controller” is distributed across each of the blades containing authorities  168 . This distribution of logical control is shown in  FIG. 2D  as a host controller  242 , mid-tier controller  244  and storage unit controller(s)  246 . Management of the control plane and the storage plane are treated independently, although parts may be physically co-located on the same blade. Each authority  168  effectively serves as an independent controller. Each authority  168  provides its own data and metadata structures, its own background workers, and maintains its own lifecycle. 
       FIG. 2E  is a blade  252  hardware block diagram, showing a control plane  254 , compute and storage planes  256 ,  258 , and authorities  168  interacting with underlying physical resources, using embodiments of the storage nodes  150  and storage units  152  of  FIGS. 2A-C  in the storage server environment of  FIG. 2D . The control plane  254  is partitioned into a number of authorities  168  which can use the compute resources in the compute plane  256  to run on any of the blades  252 . The storage plane  258  is partitioned into a set of devices, each of which provides access to flash  206  and NVRAM  204  resources. In one embodiment, the compute plane  256  may perform the operations of a storage array controller, as described herein, on one or more devices of the storage plane  258  (e.g., a storage array). 
     In the compute and storage planes  256 ,  258  of  FIG. 2E , the authorities  168  interact with the underlying physical resources (i.e., devices). From the point of view of an authority  168 , its resources are striped over all of the physical devices. From the point of view of a device, it provides resources to all authorities  168 , irrespective of where the authorities happen to run. Each authority  168  has allocated or has been allocated one or more partitions  260  of storage memory in the storage units  152 , e.g. partitions  260  in flash memory  206  and NVRAM  204 . Each authority  168  uses those allocated partitions  260  that belong to it, for writing or reading user data. Authorities can be associated with differing amounts of physical storage of the system. For example, one authority  168  could have a larger number of partitions  260  or larger sized partitions  260  in one or more storage units  152  than one or more other authorities  168 . 
       FIG. 2F  depicts elasticity software layers in blades  252  of a storage cluster, in accordance with some embodiments. In the elasticity structure, elasticity software is symmetric, i.e., each blade&#39;s compute module  270  runs the three identical layers of processes depicted in  FIG. 2F . Storage managers  274  execute read and write requests from other blades  252  for data and metadata stored in local storage unit  152  NVRAM  204  and flash  206 . Authorities  168  fulfill client requests by issuing the necessary reads and writes to the blades  252  on whose storage units  152  the corresponding data or metadata resides. Endpoints  272  parse client connection requests received from switch fabric  146  supervisory software, relay the client connection requests to the authorities  168  responsible for fulfillment, and relay the authorities&#39;  168  responses to clients. The symmetric three-layer structure enables the storage system&#39;s high degree of concurrency. Elasticity scales out efficiently and reliably in these embodiments. In addition, elasticity implements a unique scale-out technique that balances work evenly across all resources regardless of client access pattern, and maximizes concurrency by eliminating much of the need for inter-blade coordination that typically occurs with conventional distributed locking. 
     Still referring to  FIG. 2F , authorities  168  running in the compute modules  270  of a blade  252  perform the internal operations required to fulfill client requests. One feature of elasticity is that authorities  168  are stateless, i.e., they cache active data and metadata in their own blades&#39;  252  DRAMs for fast access, but the authorities store every update in their NVRAM  204  partitions on three separate blades  252  until the update has been written to flash  206 . All the storage system writes to NVRAM  204  are in triplicate to partitions on three separate blades  252  in some embodiments. With triple-mirrored NVRAM  204  and persistent storage protected by parity and Reed-Solomon RAID checksums, the storage system can survive concurrent failure of two blades  252  with no loss of data, metadata, or access to either. 
     Because authorities  168  are stateless, they can migrate between blades  252 . Each authority  168  has a unique identifier. NVRAM  204  and flash  206  partitions are associated with authorities&#39;  168  identifiers, not with the blades  252  on which they are running in some. Thus, when an authority  168  migrates, the authority  168  continues to manage the same storage partitions from its new location. When a new blade  252  is installed in an embodiment of the storage cluster, the system automatically rebalances load by: partitioning the new blade&#39;s  252  storage for use by the system&#39;s authorities  168 , migrating selected authorities  168  to the new blade  252 , starting endpoints  272  on the new blade  252  and including them in the switch fabric&#39;s  146  client connection distribution algorithm. 
     From their new locations, migrated authorities  168  persist the contents of their NVRAM  204  partitions on flash  206 , process read and write requests from other authorities  168 , and fulfill the client requests that endpoints  272  direct to them. Similarly, if a blade  252  fails or is removed, the system redistributes its authorities  168  among the system&#39;s remaining blades  252 . The redistributed authorities  168  continue to perform their original functions from their new locations. 
       FIG. 2G  depicts authorities  168  and storage resources in blades  252  of a storage cluster, in accordance with some embodiments. Each authority  168  is exclusively responsible for a partition of the flash  206  and NVRAM  204  on each blade  252 . The authority  168  manages the content and integrity of its partitions independently of other authorities  168 . Authorities  168  compress incoming data and preserve it temporarily in their NVRAM  204  partitions, and then consolidate, RAID-protect, and persist the data in segments of the storage in their flash  206  partitions. As the authorities  168  write data to flash  206 , storage managers  274  perform the necessary flash translation to optimize write performance and maximize media longevity. In the background, authorities  168  “garbage collect,” or reclaim space occupied by data that clients have made obsolete by overwriting the data. It should be appreciated that since authorities&#39;  168  partitions are disjoint, there is no need for distributed locking to execute client and writes or to perform background functions. 
     The embodiments described herein may utilize various software, communication and/or networking protocols. In addition, the configuration of the hardware and/or software may be adjusted to accommodate various protocols. For example, the embodiments may utilize Active Directory, which is a database based system that provides authentication, directory, policy, and other services in a WINDOWS™ environment. In these embodiments, LDAP (Lightweight Directory Access Protocol) is one example application protocol for querying and modifying items in directory service providers such as Active Directory. In some embodiments, a network lock manager (‘NLM’) is utilized as a facility that works in cooperation with the Network File System (‘NFS’) to provide a System V style of advisory file and record locking over a network. The Server Message Block (‘SMB’) protocol, one version of which is also known as Common Internet File System (‘CIFS’), may be integrated with the storage systems discussed herein. SMP operates as an application-layer network protocol typically used for providing shared access to files, printers, and serial ports and miscellaneous communications between nodes on a network. SMB also provides an authenticated inter-process communication mechanism. AMAZON™ S3 (Simple Storage Service) is a web service offered by Amazon Web Services, and the systems described herein may interface with Amazon S3 through web services interfaces (REST (representational state transfer), SOAP (simple object access protocol), and BitTorrent). A RESTful API (application programming interface) breaks down a transaction to create a series of small modules. Each module addresses a particular underlying part of the transaction. The control or permissions provided with these embodiments, especially for object data, may include utilization of an access control list (‘ACL’). The ACL is a list of permissions attached to an object and the ACL specifies which users or system processes are granted access to objects, as well as what operations are allowed on given objects. The systems may utilize Internet Protocol version 6 (‘IPv6’), as well as IPv4, for the communications protocol that provides an identification and location system for computers on networks and routes traffic across the Internet. The routing of packets between networked systems may include Equal-cost multi-path routing (‘ECMP’), which is a routing strategy where next-hop packet forwarding to a single destination can occur over multiple “best paths” which tie for top place in routing metric calculations. Multi-path routing can be used in conjunction with most routing protocols, because it is a per-hop decision limited to a single router. The software may support Multi-tenancy, which is an architecture in which a single instance of a software application serves multiple customers. Each customer may be referred to as a tenant. Tenants may be given the ability to customize some parts of the application, but may not customize the application&#39;s code, in some embodiments. The embodiments may maintain audit logs. An audit log is a document that records an event in a computing system. In addition to documenting what resources were accessed, audit log entries typically include destination and source addresses, a timestamp, and user login information for compliance with various regulations. The embodiments may support various key management policies, such as encryption key rotation. In addition, the system may support dynamic root passwords or some variation dynamically changing passwords. 
       FIG. 3A  sets forth a diagram of a storage system  306  that is coupled for data communications with a cloud services provider  302  in accordance with some embodiments of the present disclosure. Although depicted in less detail, the storage system  306  depicted in  FIG. 3A  may be similar to the storage systems described above with reference to  FIGS. 1A-1D  and  FIGS. 2A-2G . In some embodiments, the storage system  306  depicted in  FIG. 3A  may be embodied as a storage system that includes imbalanced active/active controllers, as a storage system that includes balanced active/active controllers, as a storage system that includes active/active controllers where less than all of each controller&#39;s resources are utilized such that each controller has reserve resources that may be used to support failover, as a storage system that includes fully active/active controllers, as a storage system that includes dataset-segregated controllers, as a storage system that includes dual-layer architectures with frontend controllers and back-end integrated storage controllers, as a storage system that includes scale-out clusters of dual-controller arrays, as well as combinations of such embodiments. 
     In the example depicted in  FIG. 3A , the storage system  306  is coupled to the cloud services provider  302  via a data communications link  304 . The data communications link  304  may be embodied as a dedicated data communications link, as a data communications pathway that is provided through the use of one or data communications networks such as a wide area network (‘WAN’) or LAN, or as some other mechanism capable of transporting digital information between the storage system  306  and the cloud services provider  302 . Such a data communications link  304  may be fully wired, fully wireless, or some aggregation of wired and wireless data communications pathways. In such an example, digital information may be exchanged between the storage system  306  and the cloud services provider  302  via the data communications link  304  using one or more data communications protocols. For example, digital information may be exchanged between the storage system  306  and the cloud services provider  302  via the data communications link  304  using the handheld device transfer protocol (‘HDTP’), hypertext transfer protocol (‘HTTP’), internet protocol (‘IP’), real-time transfer protocol (‘RTP’), transmission control protocol (‘TCP’), user datagram protocol (‘UDP’), wireless application protocol (‘WAP’), or other protocol. 
     The cloud services provider  302  depicted in  FIG. 3A  may be embodied, for example, as a system and computing environment that provides a vast array of services to users of the cloud services provider  302  through the sharing of computing resources via the data communications link  304 . The cloud services provider  302  may provide on-demand access to a shared pool of configurable computing resources such as computer networks, servers, storage, applications and services, and so on. The shared pool of configurable resources may be rapidly provisioned and released to a user of the cloud services provider  302  with minimal management effort. Generally, the user of the cloud services provider  302  is unaware of the exact computing resources utilized by the cloud services provider  302  to provide the services. Although in many cases such a cloud services provider  302  may be accessible via the Internet, readers of skill in the art will recognize that any system that abstracts the use of shared resources to provide services to a user through any data communications link may be considered a cloud services provider  302 . 
     In the example depicted in  FIG. 3A , the cloud services provider  302  may be configured to provide a variety of services to the storage system  306  and users of the storage system  306  through the implementation of various service models. For example, the cloud services provider  302  may be configured to provide services through the implementation of an infrastructure as a service (‘IaaS’) service model, through the implementation of a platform as a service (‘PaaS’) service model, through the implementation of a software as a service (‘SaaS’) service model, through the implementation of an authentication as a service (‘AaaS’) service model, through the implementation of a storage as a service model where the cloud services provider  302  offers access to its storage infrastructure for use by the storage system  306  and users of the storage system  306 , and so on. Readers will appreciate that the cloud services provider  302  may be configured to provide additional services to the storage system  306  and users of the storage system  306  through the implementation of additional service models, as the service models described above are included only for explanatory purposes and in no way represent a limitation of the services that may be offered by the cloud services provider  302  or a limitation as to the service models that may be implemented by the cloud services provider  302 . 
     In the example depicted in  FIG. 3A , the cloud services provider  302  may be embodied, for example, as a private cloud, as a public cloud, or as a combination of a private cloud and public cloud. In an embodiment in which the cloud services provider  302  is embodied as a private cloud, the cloud services provider  302  may be dedicated to providing services to a single organization rather than providing services to multiple organizations. In an embodiment where the cloud services provider  302  is embodied as a public cloud, the cloud services provider  302  may provide services to multiple organizations. In still alternative embodiments, the cloud services provider  302  may be embodied as a mix of a private and public cloud services with a hybrid cloud deployment. 
     Although not explicitly depicted in  FIG. 3A , readers will appreciate that a vast amount of additional hardware components and additional software components may be necessary to facilitate the delivery of cloud services to the storage system  306  and users of the storage system  306 . For example, the storage system  306  may be coupled to (or even include) a cloud storage gateway. Such a cloud storage gateway may be embodied, for example, as hardware-based or software-based appliance that is located on premise with the storage system  306 . Such a cloud storage gateway may operate as a bridge between local applications that are executing on the storage array  306  and remote, cloud-based storage that is utilized by the storage array  306 . Through the use of a cloud storage gateway, organizations may move primary iSCSI or NAS to the cloud services provider  302 , thereby enabling the organization to save space on their on-premises storage systems. Such a cloud storage gateway may be configured to emulate a disk array, a block-based device, a file server, or other storage system that can translate the SCSI commands, file server commands, or other appropriate command into REST-space protocols that facilitate communications with the cloud services provider  302 . 
     In order to enable the storage system  306  and users of the storage system  306  to make use of the services provided by the cloud services provider  302 , a cloud migration process may take place during which data, applications, or other elements from an organization&#39;s local systems (or even from another cloud environment) are moved to the cloud services provider  302 . In order to successfully migrate data, applications, or other elements to the cloud services provider&#39;s  302  environment, middleware such as a cloud migration tool may be utilized to bridge gaps between the cloud services provider&#39;s  302  environment and an organization&#39;s environment. Such cloud migration tools may also be configured to address potentially high network costs and long transfer times associated with migrating large volumes of data to the cloud services provider  302 , as well as addressing security concerns associated with sensitive data to the cloud services provider  302  over data communications networks. In order to further enable the storage system  306  and users of the storage system  306  to make use of the services provided by the cloud services provider  302 , a cloud orchestrator may also be used to arrange and coordinate automated tasks in pursuit of creating a consolidated process or workflow. Such a cloud orchestrator may perform tasks such as configuring various components, whether those components are cloud components or on-premises components, as well as managing the interconnections between such components. The cloud orchestrator can simplify the inter-component communication and connections to ensure that links are correctly configured and maintained. 
     In the example depicted in  FIG. 3A , and as described briefly above, the cloud services provider  302  may be configured to provide services to the storage system  306  and users of the storage system  306  through the usage of a SaaS service model, eliminating the need to install and run the application on local computers, which may simplify maintenance and support of the application. Such applications may take many forms in accordance with various embodiments of the present disclosure. For example, the cloud services provider  302  may be configured to provide access to data analytics applications to the storage system  306  and users of the storage system  306 . Such data analytics applications may be configured, for example, to receive vast amounts of telemetry data phoned home by the storage system  306 . Such telemetry data may describe various operating characteristics of the storage system  306  and may be analyzed for a vast array of purposes including, for example, to determine the health of the storage system  306 , to identify workloads that are executing on the storage system  306 , to predict when the storage system  306  will run out of various resources, to recommend configuration changes, hardware or software upgrades, workflow migrations, or other actions that may improve the operation of the storage system  306 . 
     The cloud services provider  302  may also be configured to provide access to virtualized computing environments to the storage system  306  and users of the storage system  306 . Such virtualized computing environments may be embodied, for example, as a virtual machine or other virtualized computer hardware platforms, virtual storage devices, virtualized computer network resources, and so on. Examples of such virtualized environments can include virtual machines that are created to emulate an actual computer, virtualized desktop environments that separate a logical desktop from a physical machine, virtualized file systems that allow uniform access to different types of concrete file systems, and many others. 
     For further explanation,  FIG. 3B  sets forth a diagram of a storage system  306  in accordance with some embodiments of the present disclosure. Although depicted in less detail, the storage system  306  depicted in  FIG. 3B  may be similar to the storage systems described above with reference to  FIGS. 1A-1D  and  FIGS. 2A-2G  as the storage system may include many of the components described above. 
     The storage system  306  depicted in  FIG. 3B  may include a vast amount of storage resources  308 , which may be embodied in many forms. For example, the storage resources  308  can include nano-RAM or another form of nonvolatile random access memory that utilizes carbon nanotubes deposited on a substrate, 3D crosspoint non-volatile memory, flash memory including single-level cell (‘SLC’) NAND flash, multi-level cell (‘MLC’) NAND flash, triple-level cell (‘TLC’) NAND flash, quad-level cell (‘QLC’) NAND flash, or others. Likewise, the storage resources  308  may include non-volatile magnetoresistive random-access memory (‘MRAM’), including spin transfer torque (‘STT’) MRAM. The example storage resources  308  may alternatively include non-volatile phase-change memory (‘PCM’), quantum memory that allows for the storage and retrieval of photonic quantum information, resistive random-access memory (‘ReRAM’), storage class memory (‘SCM’), or other form of storage resources, including any combination of resources described herein. Readers will appreciate that other forms of computer memories and storage devices may be utilized by the storage systems described above, including DRAM, SRAM, EEPROM, universal memory, and many others. The storage resources  308  depicted in  FIG. 3A  may be embodied in a variety of form factors, including but not limited to, dual in-line memory modules (‘DIMMs’), non-volatile dual in-line memory modules (‘NVDIMMs’), M.2, U.2, and others. 
     The storage resources  308  depicted in  FIG. 3A  may include various forms of SCM. SCM may effectively treat fast, non-volatile memory (e.g., NAND flash) as an extension of DRAM such that an entire dataset may be treated as an in-memory dataset that resides entirely in DRAM. SCM may include non-volatile media such as, for example, NAND flash. Such NAND flash may be accessed utilizing NVMe that can use the PCIe bus as its transport, providing for relatively low access latencies compared to older protocols. In fact, the network protocols used for SSDs in all-flash arrays can include NVMe using Ethernet (ROCE, NVME TCP), Fibre Channel (NVMe FC), InfiniBand (iWARP), and others that make it possible to treat fast, non-volatile memory as an extension of DRAM. In view of the fact that DRAM is often byte-addressable and fast, non-volatile memory such as NAND flash is block-addressable, a controller software/hardware stack may be needed to convert the block data to the bytes that are stored in the media. Examples of media and software that may be used as SCM can include, for example, 3D XPoint, Intel Memory Drive Technology, Samsung&#39;s Z-SSD, and others. 
     The example storage system  306  depicted in  FIG. 3B  may implement a variety of storage architectures. For example, storage systems in accordance with some embodiments of the present disclosure may utilize block storage where data is stored in blocks, and each block essentially acts as an individual hard drive. Storage systems in accordance with some embodiments of the present disclosure may utilize object storage, where data is managed as objects. Each object may include the data itself, a variable amount of metadata, and a globally unique identifier, where object storage can be implemented at multiple levels (e.g., device level, system level, interface level). Storage systems in accordance with some embodiments of the present disclosure utilize file storage in which data is stored in a hierarchical structure. Such data may be saved in files and folders, and presented to both the system storing it and the system retrieving it in the same format. 
     The example storage system  306  depicted in  FIG. 3B  may be embodied as a storage system in which additional storage resources can be added through the use of a scale-up model, additional storage resources can be added through the use of a scale-out model, or through some combination thereof. In a scale-up model, additional storage may be added by adding additional storage devices. In a scale-out model, however, additional storage nodes may be added to a cluster of storage nodes, where such storage nodes can include additional processing resources, additional networking resources, and so on. 
     The storage system  306  depicted in  FIG. 3B  also includes communications resources  310  that may be useful in facilitating data communications between components within the storage system  306 , as well as data communications between the storage system  306  and computing devices that are outside of the storage system  306 , including embodiments where those resources are separated by a relatively vast expanse. The communications resources  310  may be configured to utilize a variety of different protocols and data communication fabrics to facilitate data communications between components within the storage systems as well as computing devices that are outside of the storage system. For example, the communications resources  310  can include fibre channel (‘FC’) technologies such as FC fabrics and FC protocols that can transport SCSI commands over FC network, FC over ethernet (‘FCoE’) technologies through which FC frames are encapsulated and transmitted over Ethernet networks, InfiniBand (‘IB’) technologies in which a switched fabric topology is utilized to facilitate transmissions between channel adapters, NVM Express (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’) technologies through which non-volatile storage media attached via a PCI express (‘PCIe’) bus may be accessed, and others. In fact, the storage systems described above may, directly or indirectly, make use of neutrino communication technologies and devices through which information (including binary information) is transmitted using a beam of neutrinos. 
     The communications resources  310  can also include mechanisms for accessing storage resources  308  within the storage system  306  utilizing serial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces for connecting storage resources  308  within the storage system  306  to host bus adapters within the storage system  306 , internet small computer systems interface (‘iSCSI’) technologies to provide block-level access to storage resources  308  within the storage system  306 , and other communications resources that that may be useful in facilitating data communications between components within the storage system  306 , as well as data communications between the storage system  306  and computing devices that are outside of the storage system  306 . 
     The storage system  306  depicted in  FIG. 3B  also includes processing resources  312  that may be useful in useful in executing computer program instructions and performing other computational tasks within the storage system  306 . The processing resources  312  may include one or more ASICs that are customized for some particular purpose as well as one or more CPUs. The processing resources  312  may also include one or more DSPs, one or more FPGAs, one or more systems on a chip (‘SoCs’), or other form of processing resources  312 . The storage system  306  may utilize the storage resources  312  to perform a variety of tasks including, but not limited to, supporting the execution of software resources  314  that will be described in greater detail below. 
     The storage system  306  depicted in  FIG. 3B  also includes software resources  314  that, when executed by processing resources  312  within the storage system  306 , may perform a vast array of tasks. The software resources  314  may include, for example, one or more modules of computer program instructions that when executed by processing resources  312  within the storage system  306  are useful in carrying out various data protection techniques to preserve the integrity of data that is stored within the storage systems. Readers will appreciate that such data protection techniques may be carried out, for example, by system software executing on computer hardware within the storage system, by a cloud services provider, or in other ways. Such data protection techniques can include, for example, data archiving techniques that cause data that is no longer actively used to be moved to a separate storage device or separate storage system for long-term retention, data backup techniques through which data stored in the storage system may be copied and stored in a distinct location to avoid data loss in the event of equipment failure or some other form of catastrophe with the storage system, data replication techniques through which data stored in the storage system is replicated to another storage system such that the data may be accessible via multiple storage systems, data snapshotting techniques through which the state of data within the storage system is captured at various points in time, data and database cloning techniques through which duplicate copies of data and databases may be created, and other data protection techniques. 
     The software resources  314  may also include software that is useful in implementing software-defined storage (‘SDS’). In such an example, the software resources  314  may include one or more modules of computer program instructions that, when executed, are useful in policy-based provisioning and management of data storage that is independent of the underlying hardware. Such software resources  314  may be useful in implementing storage virtualization to separate the storage hardware from the software that manages the storage hardware. 
     The software resources  314  may also include software that is useful in facilitating and optimizing I/O operations that are directed to the storage resources  308  in the storage system  306 . For example, the software resources  314  may include software modules that perform carry out various data reduction techniques such as, for example, data compression, data deduplication, and others. The software resources  314  may include software modules that intelligently group together I/O operations to facilitate better usage of the underlying storage resource  308 , software modules that perform data migration operations to migrate from within a storage system, as well as software modules that perform other functions. Such software resources  314  may be embodied as one or more software containers or in many other ways. 
     For further explanation,  FIG. 3C  sets forth an example of a cloud-based storage system  318  in accordance with some embodiments of the present disclosure. In the example depicted in  FIG. 3C , the cloud-based storage system  318  is created entirely in a cloud computing environment  316  such as, for example, Amazon Web Services (‘AWS’), Microsoft Azure, Google Cloud Platform, IBM Cloud, Oracle Cloud, and others. The cloud-based storage system  318  may be used to provide services similar to the services that may be provided by the storage systems described above. For example, the cloud-based storage system  318  may be used to provide block storage services to users of the cloud-based storage system  318 , the cloud-based storage system  318  may be used to provide storage services to users of the cloud-based storage system  318  through the use of solid-state storage, and so on. 
     The cloud-based storage system  318  depicted in  FIG. 3C  includes two cloud computing instances  320 ,  322  that each are used to support the execution of a storage controller application  324 ,  326 . The cloud computing instances  320 ,  322  may be embodied, for example, as instances of cloud computing resources (e.g., virtual machines) that may be provided by the cloud computing environment  316  to support the execution of software applications such as the storage controller application  324 ,  326 . In one embodiment, the cloud computing instances  320 ,  322  may be embodied as Amazon Elastic Compute Cloud (‘EC2’) instances. In such an example, an Amazon Machine Image (‘AMI’) that includes the storage controller application  324 ,  326  may be booted to create and configure a virtual machine that may execute the storage controller application  324 ,  326 . 
     In the example method depicted in  FIG. 3C , the storage controller application  324 ,  326  may be embodied as a module of computer program instructions that, when executed, carries out various storage tasks. For example, the storage controller application  324 ,  326  may be embodied as a module of computer program instructions that, when executed, carries out the same tasks as the controllers  110 A,  1108  in  FIG. 1A  described above such as writing data received from the users of the cloud-based storage system  318  to the cloud-based storage system  318 , erasing data from the cloud-based storage system  318 , retrieving data from the cloud-based storage system  318  and providing such data to users of the cloud-based storage system  318 , monitoring and reporting of disk utilization and performance, performing redundancy operations, such as RAID or RAID-like data redundancy operations, compressing data, encrypting data, deduplicating data, and so forth. Readers will appreciate that because there are two cloud computing instances  320 ,  322  that each include the storage controller application  324 ,  326 , in some embodiments one cloud computing instance  320  may operate as the primary controller as described above while the other cloud computing instance  322  may operate as the secondary controller as described above. Readers will appreciate that the storage controller application  324 ,  326  depicted in  FIG. 3C  may include identical source code that is executed within different cloud computing instances  320 ,  322 . 
     Consider an example in which the cloud computing environment  316  is embodied as AWS and the cloud computing instances are embodied as EC2 instances. In such an example, the cloud computing instance  320  that operates as the primary controller may be deployed on one of the instance types that has a relatively large amount of memory and processing power while the cloud computing instance  322  that operates as the secondary controller may be deployed on one of the instance types that has a relatively small amount of memory and processing power. In such an example, upon the occurrence of a failover event where the roles of primary and secondary are switched, a double failover may actually be carried out such that: 1) a first failover event where the cloud computing instance  322  that formerly operated as the secondary controller begins to operate as the primary controller, and 2) a third cloud computing instance (not shown) that is of an instance type that has a relatively large amount of memory and processing power is spun up with a copy of the storage controller application, where the third cloud computing instance begins operating as the primary controller while the cloud computing instance  322  that originally operated as the secondary controller begins operating as the secondary controller again. In such an example, the cloud computing instance  320  that formerly operated as the primary controller may be terminated. Readers will appreciate that in alternative embodiments, the cloud computing instance  320  that is operating as the secondary controller after the failover event may continue to operate as the secondary controller and the cloud computing instance  322  that operated as the primary controller after the occurrence of the failover event may be terminated once the primary role has been assumed by the third cloud computing instance (not shown). 
     Readers will appreciate that while the embodiments described above relate to embodiments where one cloud computing instance  320  operates as the primary controller and the second cloud computing instance  322  operates as the secondary controller, other embodiments are within the scope of the present disclosure. For example, each cloud computing instance  320 ,  322  may operate as a primary controller for some portion of the address space supported by the cloud-based storage system  318 , each cloud computing instance  320 ,  322  may operate as a primary controller where the servicing of I/O operations directed to the cloud-based storage system  318  are divided in some other way, and so on. In fact, in other embodiments where costs savings may be prioritized over performance demands, only a single cloud computing instance may exist that contains the storage controller application. 
     The cloud-based storage system  318  depicted in  FIG. 3C  includes cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338 . The cloud computing instances  340   a ,  340   b ,  340   n  depicted in  FIG. 3C  may be embodied, for example, as instances of cloud computing resources that may be provided by the cloud computing environment  316  to support the execution of software applications. The cloud computing instances  340   a ,  340   b ,  340   n  of  FIG. 3C  may differ from the cloud computing instances  320 ,  322  described above as the cloud computing instances  340   a ,  340   b ,  340   n  of  FIG. 3C  have local storage  330 ,  334 ,  338  resources whereas the cloud computing instances  320 ,  322  that support the execution of the storage controller application  324 ,  326  need not have local storage resources. The cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338  may be embodied, for example, as EC2 M5 instances that include one or more SSDs, as EC2 R5 instances that include one or more SSDs, as EC2 I3 instances that include one or more SSDs, and so on. In some embodiments, the local storage  330 ,  334 ,  338  must be embodied as solid-state storage (e.g., SSDs) rather than storage that makes use of hard disk drives. 
     In the example depicted in  FIG. 3C , each of the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338  can include a software daemon  328 ,  332 ,  336  that, when executed by a cloud computing instance  340   a ,  340   b ,  340   n  can present itself to the storage controller applications  324 ,  326  as if the cloud computing instance  340   a ,  340   b ,  340   n  were a physical storage device (e.g., one or more SSDs). In such an example, the software daemon  328 ,  332 ,  336  may include computer program instructions similar to those that would normally be contained on a storage device such that the storage controller applications  324 ,  326  can send and receive the same commands that a storage controller would send to storage devices. In such a way, the storage controller applications  324 ,  326  may include code that is identical to (or substantially identical to) the code that would be executed by the controllers in the storage systems described above. In these and similar embodiments, communications between the storage controller applications  324 ,  326  and the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338  may utilize iSCSI, NVMe over TCP, messaging, a custom protocol, or in some other mechanism. 
     In the example depicted in  FIG. 3C , each of the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338  may also be coupled to block-storage  342 ,  344 ,  346  that is offered by the cloud computing environment  316 . The block-storage  342 ,  344 ,  346  that is offered by the cloud computing environment  316  may be embodied, for example, as Amazon Elastic Block Store (‘EBS’) volumes. For example, a first EBS volume may be coupled to a first cloud computing instance  340   a , a second EBS volume may be coupled to a second cloud computing instance  340   b , and a third EBS volume may be coupled to a third cloud computing instance  340   n . In such an example, the block-storage  342 ,  344 ,  346  that is offered by the cloud computing environment  316  may be utilized in a manner that is similar to how the NVRAM devices described above are utilized, as the software daemon  328 ,  332 ,  336  (or some other module) that is executing within a particular cloud comping instance  340   a ,  340   b ,  340   n  may, upon receiving a request to write data, initiate a write of the data to its attached EBS volume as well as a write of the data to its local storage  330 ,  334 ,  338  resources. In some alternative embodiments, data may only be written to the local storage  330 ,  334 ,  338  resources within a particular cloud comping instance  340   a ,  340   b ,  340   n . In an alternative embodiment, rather than using the block-storage  342 ,  344 ,  346  that is offered by the cloud computing environment  316  as NVRAM, actual RAM on each of the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338  may be used as NVRAM, thereby decreasing network utilization costs that would be associated with using an EBS volume as the NVRAM. 
     In the example depicted in  FIG. 3C , the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338  may be utilized, by cloud computing instances  320 ,  322  that support the execution of the storage controller application  324 ,  326  to service I/O operations that are directed to the cloud-based storage system  318 . Consider an example in which a first cloud computing instance  320  that is executing the storage controller application  324  is operating as the primary controller. In such an example, the first cloud computing instance  320  that is executing the storage controller application  324  may receive (directly or indirectly via the secondary controller) requests to write data to the cloud-based storage system  318  from users of the cloud-based storage system  318 . In such an example, the first cloud computing instance  320  that is executing the storage controller application  324  may perform various tasks such as, for example, deduplicating the data contained in the request, compressing the data contained in the request, determining where to the write the data contained in the request, and so on, before ultimately sending a request to write a deduplicated, encrypted, or otherwise possibly updated version of the data to one or more of the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338 . Either cloud computing instance  320 ,  322 , in some embodiments, may receive a request to read data from the cloud-based storage system  318  and may ultimately send a request to read data to one or more of the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338 . 
     Readers will appreciate that when a request to write data is received by a particular cloud computing instance  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338 , the software daemon  328 ,  332 ,  336  or some other module of computer program instructions that is executing on the particular cloud computing instance  340   a ,  340   b ,  340   n  may be configured to not only write the data to its own local storage  330 ,  334 ,  338  resources and any appropriate block-storage  342 ,  344 ,  346  that are offered by the cloud computing environment  316 , but the software daemon  328 ,  332 ,  336  or some other module of computer program instructions that is executing on the particular cloud computing instance  340   a ,  340   b ,  340   n  may also be configured to write the data to cloud-based object storage  348  that is attached to the particular cloud computing instance  340   a ,  340   b ,  340   n . The cloud-based object storage  348  that is attached to the particular cloud computing instance  340   a ,  340   b ,  340   n  may be embodied, for example, as Amazon Simple Storage Service (‘S3’) storage that is accessible by the particular cloud computing instance  340   a ,  340   b ,  340   n . In other embodiments, the cloud computing instances  320 ,  322  that each include the storage controller application  324 ,  326  may initiate the storage of the data in the local storage  330 ,  334 ,  338  of the cloud computing instances  340   a ,  340   b ,  340   n  and the cloud-based object storage  348 . 
     Readers will appreciate that, as described above, the cloud-based storage system  318  may be used to provide block storage services to users of the cloud-based storage system  318 . While the local storage  330 ,  334 ,  338  resources and the block-storage  342 ,  344 ,  346  resources that are utilized by the cloud computing instances  340   a ,  340   b ,  340   n  may support block-level access, the cloud-based object storage  348  that is attached to the particular cloud computing instance  340   a ,  340   b ,  340   n  supports only object-based access. In order to address this, the software daemon  328 ,  332 ,  336  or some other module of computer program instructions that is executing on the particular cloud computing instance  340   a ,  340   b ,  340   n  may be configured to take blocks of data, package those blocks into objects, and write the objects to the cloud-based object storage  348  that is attached to the particular cloud computing instance  340   a ,  340   b ,  340   n.    
     Consider an example in which data is written to the local storage  330 ,  334 ,  338  resources and the block-storage  342 ,  344 ,  346  resources that are utilized by the cloud computing instances  340   a ,  340   b ,  340   n  in 1 MB blocks. In such an example, assume that a user of the cloud-based storage system  318  issues a request to write data that, after being compressed and deduplicated by the storage controller application  324 ,  326  results in the need to write 5 MB of data. In such an example, writing the data to the local storage  330 ,  334 ,  338  resources and the block-storage  342 ,  344 ,  346  resources that are utilized by the cloud computing instances  340   a ,  340   b ,  340   n  is relatively straightforward as 5 blocks that are 1 MB in size are written to the local storage  330 ,  334 ,  338  resources and the block-storage  342 ,  344 ,  346  resources that are utilized by the cloud computing instances  340   a ,  340   b ,  340   n . In such an example, the software daemon  328 ,  332 ,  336  or some other module of computer program instructions that is executing on the particular cloud computing instance  340   a ,  340   b ,  340   n  may be configured to: 1) create a first object that includes the first 1 MB of data and write the first object to the cloud-based object storage  348 , 2) create a second object that includes the second 1 MB of data and write the second object to the cloud-based object storage  348 , 3) create a third object that includes the third 1 MB of data and write the third object to the cloud-based object storage  348 , and so on. As such, in some embodiments, each object that is written to the cloud-based object storage  348  may be identical (or nearly identical) in size. Readers will appreciate that in such an example, metadata that is associated with the data itself may be included in each object (e.g., the first 1 MB of the object is data and the remaining portion is metadata associated with the data). 
     Readers will appreciate that the cloud-based object storage  348  may be incorporated into the cloud-based storage system  318  to increase the durability of the cloud-based storage system  318 . Continuing with the example described above where the cloud computing instances  340   a ,  340   b ,  340   n  are EC2 instances, readers will understand that EC2 instances are only guaranteed to have a monthly uptime of 99.9% and data stored in the local instance store only persists during the lifetime of the EC2 instance. As such, relying on the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338  as the only source of persistent data storage in the cloud-based storage system  318  may result in a relatively unreliable storage system. Likewise, EBS volumes are designed for 99.999% availability. As such, even relying on EBS as the persistent data store in the cloud-based storage system  318  may result in a storage system that is not sufficiently durable. Amazon S3, however, is designed to provide 99.999999999% durability, meaning that a cloud-based storage system  318  that can incorporate S3 into its pool of storage is substantially more durable than various other options. 
     Readers will appreciate that while a cloud-based storage system  318  that can incorporate S3 into its pool of storage is substantially more durable than various other options, utilizing S3 as the primary pool of storage may result in storage system that has relatively slow response times and relatively long I/O latencies. As such, the cloud-based storage system  318  depicted in  FIG. 3C  not only stores data in S3 but the cloud-based storage system  318  also stores data in local storage  330 ,  334 ,  338  resources and block-storage  342 ,  344 ,  346  resources that are utilized by the cloud computing instances  340   a ,  340   b ,  340   n , such that read operations can be serviced from local storage  330 ,  334 ,  338  resources and the block-storage  342 ,  344 ,  346  resources that are utilized by the cloud computing instances  340   a ,  340   b ,  340   n , thereby reducing read latency when users of the cloud-based storage system  318  attempt to read data from the cloud-based storage system  318 . 
     In some embodiments, all data that is stored by the cloud-based storage system  318  may be stored in both: 1) the cloud-based object storage  348 , and 2) at least one of the local storage  330 ,  334 ,  338  resources or block-storage  342 ,  344 ,  346  resources that are utilized by the cloud computing instances  340   a ,  340   b ,  340   n . In such embodiments, the local storage  330 ,  334 ,  338  resources and block-storage  342 ,  344 ,  346  resources that are utilized by the cloud computing instances  340   a ,  340   b ,  340   n  may effectively operate as cache that generally includes all data that is also stored in S3, such that all reads of data may be serviced by the cloud computing instances  340   a ,  340   b ,  340   n  without requiring the cloud computing instances  340   a ,  340   b ,  340   n  to access the cloud-based object storage  348 . Readers will appreciate that in other embodiments, however, all data that is stored by the cloud-based storage system  318  may be stored in the cloud-based object storage  348 , but less than all data that is stored by the cloud-based storage system  318  may be stored in at least one of the local storage  330 ,  334 ,  338  resources or block-storage  342 ,  344 ,  346  resources that are utilized by the cloud computing instances  340   a ,  340   b ,  340   n . In such an example, various policies may be utilized to determine which subset of the data that is stored by the cloud-based storage system  318  should reside in both: 1) the cloud-based object storage  348 , and 2) at least one of the local storage  330 ,  334 ,  338  resources or block-storage  342 ,  344 ,  346  resources that are utilized by the cloud computing instances  340   a ,  340   b ,  340   n.    
     As described above, when the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338  are embodied as EC2 instances, the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338  are only guaranteed to have a monthly uptime of 99.9% and data stored in the local instance store only persists during the lifetime of each cloud computing instance  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338 . As such, one or more modules of computer program instructions that are executing within the cloud-based storage system  318  (e.g., a monitoring module that is executing on its own EC2 instance) may be designed to handle the failure of one or more of the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338 . In such an example, the monitoring module may handle the failure of one or more of the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338  by creating one or more new cloud computing instances with local storage, retrieving data that was stored on the failed cloud computing instances  340   a ,  340   b ,  340   n  from the cloud-based object storage  348 , and storing the data retrieved from the cloud-based object storage  348  in local storage on the newly created cloud computing instances. Readers will appreciate that many variants of this process may be implemented. 
     Consider an example in which all cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338  failed. In such an example, the monitoring module may create new cloud computing instances with local storage, where high-bandwidth instances types are selected that allow for the maximum data transfer rates between the newly created high-bandwidth cloud computing instances with local storage and the cloud-based object storage  348 . Readers will appreciate that instances types are selected that allow for the maximum data transfer rates between the new cloud computing instances and the cloud-based object storage  348  such that the new high-bandwidth cloud computing instances can be rehydrated with data from the cloud-based object storage  348  as quickly as possible. Once the new high-bandwidth cloud computing instances are rehydrated with data from the cloud-based object storage  348 , less expensive lower-bandwidth cloud computing instances may be created, data may be migrated to the less expensive lower-bandwidth cloud computing instances, and the high-bandwidth cloud computing instances may be terminated. 
     Readers will appreciate that in some embodiments, the number of new cloud computing instances that are created may substantially exceed the number of cloud computing instances that are needed to locally store all of the data stored by the cloud-based storage system  318 . The number of new cloud computing instances that are created may substantially exceed the number of cloud computing instances that are needed to locally store all of the data stored by the cloud-based storage system  318  in order to more rapidly pull data from the cloud-based object storage  348  and into the new cloud computing instances, as each new cloud computing instance can (in parallel) retrieve some portion of the data stored by the cloud-based storage system  318 . In such embodiments, once the data stored by the cloud-based storage system  318  has been pulled into the newly created cloud computing instances, the data may be consolidated within a subset of the newly created cloud computing instances and those newly created cloud computing instances that are excessive may be terminated. 
     Consider an example in which 1000 cloud computing instances are needed in order to locally store all valid data that users of the cloud-based storage system  318  have written to the cloud-based storage system  318 . In such an example, assume that all 1,000 cloud computing instances fail. In such an example, the monitoring module may cause 100,000 cloud computing instances to be created, where each cloud computing instance is responsible for retrieving, from the cloud-based object storage  348 , distinct 1/100,000th chunks of the valid data that users of the cloud-based storage system  318  have written to the cloud-based storage system  318  and locally storing the distinct chunk of the dataset that it retrieved. In such an example, because each of the 100,000 cloud computing instances can retrieve data from the cloud-based object storage  348  in parallel, the caching layer may be restored 100 times faster as compared to an embodiment where the monitoring module only create 1000 replacement cloud computing instances. In such an example, over time the data that is stored locally in the 100,000 could be consolidated into 1,000 cloud computing instances and the remaining 99,000 cloud computing instances could be terminated. 
     Readers will appreciate that various performance aspects of the cloud-based storage system  318  may be monitored (e.g., by a monitoring module that is executing in an EC2 instance) such that the cloud-based storage system  318  can be scaled-up or scaled-out as needed. Consider an example in which the monitoring module monitors the performance of the could-based storage system  318  via communications with one or more of the cloud computing instances  320 ,  322  that each are used to support the execution of a storage controller application  324 ,  326 , via monitoring communications between cloud computing instances  320 ,  322 ,  340   a ,  340   b ,  340   n , via monitoring communications between cloud computing instances  320 ,  322 ,  340   a ,  340   b ,  340   n  and the cloud-based object storage  348 , or in some other way. In such an example, assume that the monitoring module determines that the cloud computing instances  320 ,  322  that are used to support the execution of a storage controller application  324 ,  326  are undersized and not sufficiently servicing the I/O requests that are issued by users of the cloud-based storage system  318 . In such an example, the monitoring module may create a new, more powerful cloud computing instance (e.g., a cloud computing instance of a type that includes more processing power, more memory, etc. . . . ) that includes the storage controller application such that the new, more powerful cloud computing instance can begin operating as the primary controller. Likewise, if the monitoring module determines that the cloud computing instances  320 ,  322  that are used to support the execution of a storage controller application  324 ,  326  are oversized and that cost savings could be gained by switching to a smaller, less powerful cloud computing instance, the monitoring module may create a new, less powerful (and less expensive) cloud computing instance that includes the storage controller application such that the new, less powerful cloud computing instance can begin operating as the primary controller. 
     Consider, as an additional example of dynamically sizing the cloud-based storage system  318 , an example in which the monitoring module determines that the utilization of the local storage that is collectively provided by the cloud computing instances  340   a ,  340   b ,  340   n  has reached a predetermined utilization threshold (e.g., 95%). In such an example, the monitoring module may create additional cloud computing instances with local storage to expand the pool of local storage that is offered by the cloud computing instances. Alternatively, the monitoring module may create one or more new cloud computing instances that have larger amounts of local storage than the already existing cloud computing instances  340   a ,  340   b ,  340   n , such that data stored in an already existing cloud computing instance  340   a ,  340   b ,  340   n  can be migrated to the one or more new cloud computing instances and the already existing cloud computing instance  340   a ,  340   b ,  340   n  can be terminated, thereby expanding the pool of local storage that is offered by the cloud computing instances. Likewise, if the pool of local storage that is offered by the cloud computing instances is unnecessarily large, data can be consolidated and some cloud computing instances can be terminated. 
     Readers will appreciate that the cloud-based storage system  318  may be sized up and down automatically by a monitoring module applying a predetermined set of rules that may be relatively simple of relatively complicated. In fact, the monitoring module may not only take into account the current state of the cloud-based storage system  318 , but the monitoring module may also apply predictive policies that are based on, for example, observed behavior (e.g., every night from 10 PM until 6 AM usage of the storage system is relatively light), predetermined fingerprints (e.g., every time a virtual desktop infrastructure adds 100 virtual desktops, the number of IOPS directed to the storage system increase by X), and so on. In such an example, the dynamic scaling of the cloud-based storage system  318  may be based on current performance metrics, predicted workloads, and many other factors, including combinations thereof. 
     Readers will further appreciate that because the cloud-based storage system  318  may be dynamically scaled, the cloud-based storage system  318  may even operate in a way that is more dynamic. Consider the example of garbage collection. In a traditional storage system, the amount of storage is fixed. As such, at some point the storage system may be forced to perform garbage collection as the amount of available storage has become so constrained that the storage system is on the verge of running out of storage. In contrast, the cloud-based storage system  318  described here can always ‘add’ additional storage (e.g., by adding more cloud computing instances with local storage). Because the cloud-based storage system  318  described here can always ‘add’ additional storage, the cloud-based storage system  318  can make more intelligent decisions regarding when to perform garbage collection. For example, the cloud-based storage system  318  may implement a policy that garbage collection only be performed when the number of IOPS being serviced by the cloud-based storage system  318  falls below a certain level. In some embodiments, other system-level functions (e.g., deduplication, compression) may also be turned off and on in response to system load, given that the size of the cloud-based storage system  318  is not constrained in the same way that traditional storage systems are constrained. 
     Readers will appreciate that embodiments of the present disclosure resolve an issue with block-storage services offered by some cloud computing environments as some cloud computing environments only allow for one cloud computing instance to connect to a block-storage volume at a single time. For example, in Amazon AWS, only a single EC2 instance may be connected to an EBS volume. Through the use of EC2 instances with local storage, embodiments of the present disclosure can offer multi-connect capabilities where multiple EC2 instances can connect to another EC2 instance with local storage (‘a drive instance’). In such embodiments, the drive instances may include software executing within the drive instance that allows the drive instance to support I/O directed to a particular volume from each connected EC2 instance. As such, some embodiments of the present disclosure may be embodied as multi-connect block storage services that may not include all of the components depicted in  FIG. 3C . 
     In some embodiments, especially in embodiments where the cloud-based object storage  348  resources are embodied as Amazon S3, the cloud-based storage system  318  may include one or more modules (e.g., a module of computer program instructions executing on an EC2 instance) that are configured to ensure that when the local storage of a particular cloud computing instance is rehydrated with data from S3, the appropriate data is actually in S3. This issue arises largely because S3 implements an eventual consistency model where, when overwriting an existing object, reads of the object will eventually (but not necessarily immediately) become consistent and will eventually (but not necessarily immediately) return the overwritten version of the object. To address this issue, in some embodiments of the present disclosure, objects in S3 are never overwritten. Instead, a traditional ‘overwrite’ would result in the creation of the new object (that includes the updated version of the data) and the eventual deletion of the old object (that includes the previous version of the data). 
     In some embodiments of the present disclosure, as part of an attempt to never (or almost never) overwrite an object, when data is written to S3 the resultant object may be tagged with a sequence number. In some embodiments, these sequence numbers may be persisted elsewhere (e.g., in a database) such that at any point in time, the sequence number associated with the most up-to-date version of some piece of data can be known. In such a way, a determination can be made as to whether S3 has the most recent version of some piece of data by merely reading the sequence number associated with an object—and without actually reading the data from S3. The ability to make this determination may be particularly important when a cloud computing instance with local storage crashes, as it would be undesirable to rehydrate the local storage of a replacement cloud computing instance with out-of-date data. In fact, because the cloud-based storage system  318  does not need to access the data to verify its validity, the data can stay encrypted and access charges can be avoided. 
     The storage systems described above may carry out intelligent data backup techniques through which data stored in the storage system may be copied and stored in a distinct location to avoid data loss in the event of equipment failure or some other form of catastrophe. For example, the storage systems described above may be configured to examine each backup to avoid restoring the storage system to an undesirable state. Consider an example in which malware infects the storage system. In such an example, the storage system may include software resources  314  that can scan each backup to identify backups that were captured before the malware infected the storage system and those backups that were captured after the malware infected the storage system. In such an example, the storage system may restore itself from a backup that does not include the malware—or at least not restore the portions of a backup that contained the malware. In such an example, the storage system may include software resources  314  that can scan each backup to identify the presences of malware (or a virus, or some other undesirable), for example, by identifying write operations that were serviced by the storage system and originated from a network subnet that is suspected to have delivered the malware, by identifying write operations that were serviced by the storage system and originated from a user that is suspected to have delivered the malware, by identifying write operations that were serviced by the storage system and examining the content of the write operation against fingerprints of the malware, and in many other ways. 
     Readers will further appreciate that the backups (often in the form of one or more snapshots) may also be utilized to perform rapid recovery of the storage system. Consider an example in which the storage system is infected with ransomware that locks users out of the storage system. In such an example, software resources  314  within the storage system may be configured to detect the presence of ransomware and may be further configured to restore the storage system to a point-in-time, using the retained backups, prior to the point-in-time at which the ransomware infected the storage system. In such an example, the presence of ransomware may be explicitly detected through the use of software tools utilized by the system, through the use of a key (e.g., a USB drive) that is inserted into the storage system, or in a similar way. Likewise, the presence of ransomware may be inferred in response to system activity meeting a predetermined fingerprint such as, for example, no reads or writes coming into the system for a predetermined period of time. 
     Readers will appreciate that the various components described above may be grouped into one or more optimized computing packages as converged infrastructures. Such converged infrastructures may include pools of computers, storage and networking resources that can be shared by multiple applications and managed in a collective manner using policy-driven processes. Such converged infrastructures may be implemented with a converged infrastructure reference architecture, with standalone appliances, with a software driven hyper-converged approach (e.g., hyper-converged infrastructures), or in other ways. 
     Readers will appreciate that the storage systems described above may be useful for supporting various types of software applications. For example, the storage system  306  may be useful in supporting artificial intelligence (‘AI’) applications, database applications, DevOps projects, electronic design automation tools, event-driven software applications, high performance computing applications, simulation applications, high-speed data capture and analysis applications, machine learning applications, media production applications, media serving applications, picture archiving and communication systems (‘PACS’) applications, software development applications, virtual reality applications, augmented reality applications, and many other types of applications by providing storage resources to such applications. 
     The storage systems described above may operate to support a wide variety of applications. In view of the fact that the storage systems include compute resources, storage resources, and a wide variety of other resources, the storage systems may be well suited to support applications that are resource intensive such as, for example, AI applications. AI applications may be deployed in a variety of fields, including: predictive maintenance in manufacturing and related fields, healthcare applications such as patient data &amp; risk analytics, retail and marketing deployments (e.g., search advertising, social media advertising), supply chains solutions, fintech solutions such as business analytics &amp; reporting tools, operational deployments such as real-time analytics tools, application performance management tools, IT infrastructure management tools, and many others. 
     Such AI applications may enable devices to perceive their environment and take actions that maximize their chance of success at some goal. Examples of such AI applications can include IBM Watson, Microsoft Oxford, Google DeepMind, Baidu Minwa, and others. The storage systems described above may also be well suited to support other types of applications that are resource intensive such as, for example, machine learning applications. Machine learning applications may perform various types of data analysis to automate analytical model building. Using algorithms that iteratively learn from data, machine learning applications can enable computers to learn without being explicitly programmed. One particular area of machine learning is referred to as reinforcement learning, which involves taking suitable actions to maximize reward in a particular situation. Reinforcement learning may be employed to find the best possible behavior or path that a particular software application or machine should take in a specific situation. Reinforcement learning differs from other areas of machine learning (e.g., supervised learning, unsupervised learning) in that correct input/output pairs need not be presented for reinforcement learning and sub-optimal actions need not be explicitly corrected. 
     In addition to the resources already described, the storage systems described above may also include graphics processing units (‘GPUs’), occasionally referred to as visual processing unit (‘VPUs’). Such GPUs may be embodied as specialized electronic circuits that rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. Such GPUs may be included within any of the computing devices that are part of the storage systems described above, including as one of many individually scalable components of a storage system, where other examples of individually scalable components of such storage system can include storage components, memory components, compute components (e.g., CPUs, FPGAs, ASICs), networking components, software components, and others. In addition to GPUs, the storage systems described above may also include neural network processors (‘NNPs’) for use in various aspects of neural network processing. Such NNPs may be used in place of (or in addition to) GPUs and may be also be independently scalable. 
     As described above, the storage systems described herein may be configured to support artificial intelligence applications, machine learning applications, big data analytics applications, and many other types of applications. The rapid growth in these sort of applications is being driven by three technologies: deep learning (DL), GPU processors, and Big Data. Deep learning is a computing model that makes use of massively parallel neural networks inspired by the human brain. Instead of experts handcrafting software, a deep learning model writes its own software by learning from lots of examples. Such GPUs may include thousands of cores that are well-suited to run algorithms that loosely represent the parallel nature of the human brain. 
     Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to tap into their data with artificial intelligence (AI). With improved algorithms, larger data sets, and various frameworks (including open-source software libraries for machine learning across a range of tasks), data scientists are tackling new use cases like autonomous driving vehicles, natural language processing and understanding, computer vision, machine reasoning, strong AI, and many others. Applications of such techniques may include: machine and vehicular object detection, identification and avoidance; visual recognition, classification and tagging; algorithmic financial trading strategy performance management; simultaneous localization and mapping; predictive maintenance of high-value machinery; prevention against cyber security threats, expertise automation; image recognition and classification; question answering; robotics; text analytics (extraction, classification) and text generation and translation; and many others. Applications of AI techniques has materialized in a wide array of products include, for example, Amazon Echo&#39;s speech recognition technology that allows users to talk to their machines, Google Translate™ which allows for machine-based language translation, Spotify&#39;s Discover Weekly that provides recommendations on new songs and artists that a user may like based on the user&#39;s usage and traffic analysis, Quill&#39;s text generation offering that takes structured data and turns it into narrative stories, Chatbots that provide real-time, contextually specific answers to questions in a dialog format, and many others. 
     Data is the heart of modern AI and deep learning algorithms. Before training can begin, one problem that must be addressed revolves around collecting the labeled data that is crucial for training an accurate AI model. A full scale AI deployment may be required to continuously collect, clean, transform, label, and store large amounts of data. Adding additional high quality data points directly translates to more accurate models and better insights. Data samples may undergo a series of processing steps including, but not limited to: 1) ingesting the data from an external source into the training system and storing the data in raw form, 2) cleaning and transforming the data in a format convenient for training, including linking data samples to the appropriate label, 3) exploring parameters and models, quickly testing with a smaller dataset, and iterating to converge on the most promising models to push into the production cluster, 4) executing training phases to select random batches of input data, including both new and older samples, and feeding those into production GPU servers for computation to update model parameters, and 5) evaluating including using a holdback portion of the data not used in training in order to evaluate model accuracy on the holdout data. This lifecycle may apply for any type of parallelized machine learning, not just neural networks or deep learning. For example, standard machine learning frameworks may rely on CPUs instead of GPUs but the data ingest and training workflows may be the same. Readers will appreciate that a single shared storage data hub creates a coordination point throughout the lifecycle without the need for extra data copies among the ingest, preprocessing, and training stages. Rarely is the ingested data used for only one purpose, and shared storage gives the flexibility to train multiple different models or apply traditional analytics to the data. 
     Readers will appreciate that each stage in the AI data pipeline may have varying requirements from the data hub (e.g., the storage system or collection of storage systems). Scale-out storage systems must deliver uncompromising performance for all manner of access types and patterns—from small, metadata-heavy to large files, from random to sequential access patterns, and from low to high concurrency. The storage systems described above may serve as an ideal AI data hub as the systems may service unstructured workloads. In the first stage, data is ideally ingested and stored on to the same data hub that following stages will use, in order to avoid excess data copying. The next two steps can be done on a standard compute server that optionally includes a GPU, and then in the fourth and last stage, full training production jobs are run on powerful GPU-accelerated servers. Often, there is a production pipeline alongside an experimental pipeline operating on the same dataset. Further, the GPU-accelerated servers can be used independently for different models or joined together to train on one larger model, even spanning multiple systems for distributed training. If the shared storage tier is slow, then data must be copied to local storage for each phase, resulting in wasted time staging data onto different servers. The ideal data hub for the AI training pipeline delivers performance similar to data stored locally on the server node while also having the simplicity and performance to enable all pipeline stages to operate concurrently. 
     Although the preceding paragraphs discuss deep learning applications, readers will appreciate that the storage systems described herein may also be part of a distributed deep learning (‘DDL’) platform to support the execution of DDL algorithms. The storage systems described above may also be paired with other technologies such as TensorFlow, an open-source software library for dataflow programming across a range of tasks that may be used for machine learning applications such as neural networks, to facilitate the development of such machine learning models, applications, and so on. 
     The storage systems described above may also be used in a neuromorphic computing environment. Neuromorphic computing is a form of computing that mimics brain cells. To support neuromorphic computing, an architecture of interconnected “neurons” replace traditional computing models with low-powered signals that go directly between neurons for more efficient computation. Neuromorphic computing may make use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system, as well as analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems for perception, motor control, or multisensory integration. 
     Readers will appreciate that the storage systems described above may be configured to support the storage or use of (among other types of data) blockchains. In addition to supporting the storage and use of blockchain technologies, the storage systems described above may also support the storage and use of derivative items such as, for example, open source blockchains and related tools that are part of the IBM™ Hyperledger project, permissioned blockchains in which a certain number of trusted parties are allowed to access the block chain, blockchain products that enable developers to build their own distributed ledger projects, and others. Blockchains and the storage systems described herein may be leveraged to support on-chain storage of data as well as off-chain storage of data. 
     Off-chain storage of data can be implemented in a variety of ways and can occur when the data itself is not stored within the blockchain. For example, in one embodiment, a hash function may be utilized and the data itself may be fed into the hash function to generate a hash value. In such an example, the hashes of large pieces of data may be embedded within transactions, instead of the data itself. Readers will appreciate that, in other embodiments, alternatives to blockchains may be used to facilitate the decentralized storage of information. For example, one alternative to a blockchain that may be used is a blockweave. While conventional blockchains store every transaction to achieve validation, a blockweave permits secure decentralization without the usage of the entire chain, thereby enabling low cost on-chain storage of data. Such blockweaves may utilize a consensus mechanism that is based on proof of access (PoA) and proof of work (PoW). 
     The storage systems described above may, either alone or in combination with other computing devices, be used to support in-memory computing applications. In-memory computing involves the storage of information in RAM that is distributed across a cluster of computers. Readers will appreciate that the storage systems described above, especially those that are configurable with customizable amounts of processing resources, storage resources, and memory resources (e.g., those systems in which blades that contain configurable amounts of each type of resource), may be configured in a way so as to provide an infrastructure that can support in-memory computing. Likewise, the storage systems described above may include component parts (e.g., NVDIMMs, 3D crosspoint storage that provide fast random access memory that is persistent) that can actually provide for an improved in-memory computing environment as compared to in-memory computing environments that rely on RAM distributed across dedicated servers. 
     In some embodiments, the storage systems described above may be configured to operate as a hybrid in-memory computing environment that includes a universal interface to all storage media (e.g., RAM, flash storage, 3D crosspoint storage). In such embodiments, users may have no knowledge regarding the details of where their data is stored but they can still use the same full, unified API to address data. In such embodiments, the storage system may (in the background) move data to the fastest layer available—including intelligently placing the data in dependence upon various characteristics of the data or in dependence upon some other heuristic. In such an example, the storage systems may even make use of existing products such as Apache Ignite and GridGain to move data between the various storage layers, or the storage systems may make use of custom software to move data between the various storage layers. The storage systems described herein may implement various optimizations to improve the performance of in-memory computing such as, for example, having computations occur as close to the data as possible. 
     Readers will further appreciate that in some embodiments, the storage systems described above may be paired with other resources to support the applications described above. For example, one infrastructure could include primary compute in the form of servers and workstations which specialize in using General-purpose computing on graphics processing units (‘GPGPU’) to accelerate deep learning applications that are interconnected into a computation engine to train parameters for deep neural networks. Each system may have Ethernet external connectivity, InfiniBand external connectivity, some other form of external connectivity, or some combination thereof. In such an example, the GPUs can be grouped for a single large training or used independently to train multiple models. The infrastructure could also include a storage system such as those described above to provide, for example, a scale-out all-flash file or object store through which data can be accessed via high-performance protocols such as NFS, S3, and so on. The infrastructure can also include, for example, redundant top-of-rack Ethernet switches connected to storage and compute via ports in MLAG port channels for redundancy. The infrastructure could also include additional compute in the form of whitebox servers, optionally with GPUs, for data ingestion, pre-processing, and model debugging. Readers will appreciate that additional infrastructures are also be possible. 
     Readers will appreciate that the storage systems described above, either alone or in coordination with other computing machinery may be configured to support other AI related tools. For example, the storage systems may make use of tools like ONXX or other open neural network exchange formats that make it easier to transfer models written in different AI frameworks. Likewise, the storage systems may be configured to support tools like Amazon&#39;s Gluon that allow developers to prototype, build, and train deep learning models. In fact, the storage systems described above may be part of a larger platform, such as IBM™ Cloud Private for Data, that includes integrated data science, data engineering and application building services. 
     Readers will further appreciate that the storage systems described above may also be deployed as an edge solution. Such an edge solution may be in place to optimize cloud computing systems by performing data processing at the edge of the network, near the source of the data. Edge computing can push applications, data and computing power (i.e., services) away from centralized points to the logical extremes of a network. Through the use of edge solutions such as the storage systems described above, computational tasks may be performed using the compute resources provided by such storage systems, data may be storage using the storage resources of the storage system, and cloud-based services may be accessed through the use of various resources of the storage system (including networking resources). By performing computational tasks on the edge solution, storing data on the edge solution, and generally making use of the edge solution, the consumption of expensive cloud-based resources may be avoided and, in fact, performance improvements may be experienced relative to a heavier reliance on cloud-based resources. 
     While many tasks may benefit from the utilization of an edge solution, some particular uses may be especially suited for deployment in such an environment. For example, devices like drones, autonomous cars, robots, and others may require extremely rapid processing—so fast, in fact, that sending data up to a cloud environment and back to receive data processing support may simply be too slow. As an additional example, some IoT devices such as connected video cameras may not be well-suited for the utilization of cloud-based resources as it may be impractical (not only from a privacy perspective, security perspective, or a financial perspective) to send the data to the cloud simply because of the pure volume of data that is involved. As such, many tasks that really on data processing, storage, or communications may be better suited by platforms that include edge solutions such as the storage systems described above. 
     The storage systems described above may alone, or in combination with other computing resources, serves as a network edge platform that combines compute resources, storage resources, networking resources, cloud technologies and network virtualization technologies, and so on. As part of the network, the edge may take on characteristics similar to other network facilities, from the customer premise and backhaul aggregation facilities to Points of Presence (PoPs) and regional data centers. Readers will appreciate that network workloads, such as Virtual Network Functions (VNFs) and others, will reside on the network edge platform. Enabled by a combination of containers and virtual machines, the network edge platform may rely on controllers and schedulers that are no longer geographically co-located with the data processing resources. The functions, as microservices, may split into control planes, user and data planes, or even state machines, allowing for independent optimization and scaling techniques to be applied. Such user and data planes may be enabled through increased accelerators, both those residing in server platforms, such as FPGAs and Smart NICs, and through SDN-enabled merchant silicon and programmable ASICs. 
     The storage systems described above may also be optimized for use in big data analytics. Big data analytics may be generally described as the process of examining large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. As part of that process, semi-structured and unstructured data such as, for example, internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile-phone call-detail records, IoT sensor data, and other data may be converted to a structured form. 
     The storage systems described above may also support (including implementing as a system interface) applications that perform tasks in response to human speech. For example, the storage systems may support the execution intelligent personal assistant applications such as, for example, Amazon&#39;s Alexa, Apple Siri, Google Voice, Samsung Bixby, Microsoft Cortana, and others. While the examples described in the previous sentence make use of voice as input, the storage systems described above may also support chatbots, talkbots, chatterbots, or artificial conversational entities or other applications that are configured to conduct a conversation via auditory or textual methods. Likewise, the storage system may actually execute such an application to enable a user such as a system administrator to interact with the storage system via speech. Such applications are generally capable of voice interaction, music playback, making to-do lists, setting alarms, streaming podcasts, playing audiobooks, and providing weather, traffic, and other real time information, such as news, although in embodiments in accordance with the present disclosure, such applications may be utilized as interfaces to various system management operations. 
     The storage systems described above may also implement AI platforms for delivering on the vision of self-driving storage. Such AI platforms may be configured to deliver global predictive intelligence by collecting and analyzing large amounts of storage system telemetry data points to enable effortless management, analytics and support. In fact, such storage systems may be capable of predicting both capacity and performance, as well as generating intelligent advice on workload deployment, interaction and optimization. Such AI platforms may be configured to scan all incoming storage system telemetry data against a library of issue fingerprints to predict and resolve incidents in real-time, before they impact customer environments, and captures hundreds of variables related to performance that are used to forecast performance load. 
     The storage systems described above may support the serialized or simultaneous execution of artificial intelligence applications, machine learning applications, data analytics applications, data transformations, and other tasks that collectively may form an AI ladder. Such an AI ladder may effectively be formed by combining such elements to form a complete data science pipeline, where exist dependencies between elements of the AI ladder. For example, AI may require that some form of machine learning has taken place, machine learning may require that some form of analytics has taken place, analytics may require that some form of data and information architecting has taken place, and so on. As such, each element may be viewed as a rung in an AI ladder that collectively can form a complete and sophisticated AI solution. 
     The storage systems described above may also, either alone or in combination with other computing environments, be used to deliver an AI everywhere experience where AI permeates wide and expansive aspects of business and life. For example, AI may play an important role in the delivery of deep learning solutions, deep reinforcement learning solutions, artificial general intelligence solutions, autonomous vehicles, cognitive computing solutions, commercial UAVs or drones, conversational user interfaces, enterprise taxonomies, ontology management solutions, machine learning solutions, smart dust, smart robots, smart workplaces, and many others. 
     The storage systems described above may also, either alone or in combination with other computing environments, be used to deliver a wide range of transparently immersive experiences (including those that use digital twins of various “things” such as people, places, processes, systems, and so on) where technology can introduce transparency between people, businesses, and things. Such transparently immersive experiences may be delivered as augmented reality technologies, connected homes, virtual reality technologies, brain-computer interfaces, human augmentation technologies, nanotube electronics, volumetric displays, 4D printing technologies, or others. 
     The storage systems described above may also, either alone or in combination with other computing environments, be used to support a wide variety of digital platforms. Such digital platforms can include, for example, 5G wireless systems and platforms, digital twin platforms, edge computing platforms, IoT platforms, quantum computing platforms, serverless PaaS, software-defined security, neuromorphic computing platforms, and so on. 
     The storage systems described above may also be part of a multi-cloud environment in which multiple cloud computing and storage services are deployed in a single heterogeneous architecture. In order to facilitate the operation of such a multi-cloud environment, DevOps tools may be deployed to enable orchestration across clouds. Likewise, continuous development and continuous integration tools may be deployed to standardize processes around continuous integration and delivery, new feature rollout and provisioning cloud workloads. By standardizing these processes, a multi-cloud strategy may be implemented that enables the utilization of the best provider for each workload. 
     The storage systems described above may be used as a part of a platform to enable the use of crypto-anchors that may be used to authenticate a product&#39;s origins and contents to ensure that it matches a blockchain record associated with the product. Similarly, as part of a suite of tools to secure data stored on the storage system, the storage systems described above may implement various encryption technologies and schemes, including lattice cryptography. Lattice cryptography can involve constructions of cryptographic primitives that involve lattices, either in the construction itself or in the security proof. Unlike public-key schemes such as the RSA, Diffie-Hellman or Elliptic-Curve cryptosystems, which are easily attacked by a quantum computer, some lattice-based constructions appear to be resistant to attack by both classical and quantum computers. 
     A quantum computer is a device that performs quantum computing. Quantum computing is computing using quantum-mechanical phenomena, such as superposition and entanglement. Quantum computers differ from traditional computers that are based on transistors, as such traditional computers require that data be encoded into binary digits (bits), each of which is always in one of two definite states (0 or 1). In contrast to traditional computers, quantum computers use quantum bits, which can be in superpositions of states. A quantum computer maintains a sequence of qubits, where a single qubit can represent a one, a zero, or any quantum superposition of those two qubit states. A pair of qubits can be in any quantum superposition of 4 states, and three qubits in any superposition of 8 states. A quantum computer with n qubits can generally be in an arbitrary superposition of up to 2{circumflex over ( )}n different states simultaneously, whereas a traditional computer can only be in one of these states at any one time. A quantum Turing machine is a theoretical model of such a computer. 
     The storage systems described above may also be paired with FPGA-accelerated servers as part of a larger AI or ML infrastructure. Such FPGA-accelerated servers may reside near (e.g., in the same data center) the storage systems described above or even incorporated into an appliance that includes one or more storage systems, one or more FPGA-accelerated servers, networking infrastructure that supports communications between the one or more storage systems and the one or more FPGA-accelerated servers, as well as other hardware and software components. Alternatively, FPGA-accelerated servers may reside within a cloud computing environment that may be used to perform compute-related tasks for AI and ML jobs. Any of the embodiments described above may be used to collectively serve as a FPGA-based AI or ML platform. Readers will appreciate that, in some embodiments of the FPGA-based AI or ML platform, the FPGAs that are contained within the FPGA-accelerated servers may be reconfigured for different types of ML models (e.g., LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that are contained within the FPGA-accelerated servers may enable the acceleration of a ML or AI application based on the most optimal numerical precision and memory model being used. Readers will appreciate that by treating the collection of FPGA-accelerated servers as a pool of FPGAs, any CPU in the data center may utilize the pool of FPGAs as a shared hardware microservice, rather than limiting a server to dedicated accelerators plugged into it. 
     The FPGA-accelerated servers and the GPU-accelerated servers described above may implement a model of computing where, rather than keeping a small amount of data in a CPU and running a long stream of instructions over it as occurred in more traditional computing models, the machine learning model and parameters are pinned into the high-bandwidth on-chip memory with lots of data streaming though the high-bandwidth on-chip memory. FPGAs may even be more efficient than GPUs for this computing model, as the FPGAs can be programmed with only the instructions needed to run this kind of computing model. 
     The storage systems described above may be configured to provide parallel storage, for example, through the use of a parallel file system such as BeeGFS. Such parallel files systems may include a distributed metadata architecture. For example, the parallel file system may include a plurality of metadata servers across which metadata is distributed, as well as components that include services for clients and storage servers. 
     The systems described above can support the execution of a wide array of software applications. Such software applications can be deployed in a variety of ways, including container-based deployment models. Containerized applications may be managed using a variety of tools. For example, containerized applications may be managed using Docker Swarm, Kubernetes, and others. Containerized applications may be used to facilitate a serverless, cloud native computing deployment and management model for software applications. In support of a serverless, cloud native computing deployment and management model for software applications, containers may be used as part of an event handling mechanisms (e.g., AWS Lambdas) such that various events cause a containerized application to be spun up to operate as an event handler. 
     The systems described above may be deployed in a variety of ways, including being deployed in ways that support fifth generation (‘5G’) networks. 5G networks may support substantially faster data communications than previous generations of mobile communications networks and, as a consequence may lead to the disaggregation of data and computing resources as modern massive data centers may become less prominent and may be replaced, for example, by more-local, micro data centers that are close to the mobile-network towers. The systems described above may be included in such local, micro data centers and may be part of or paired to multi-access edge computing (‘MEC’) systems. Such MEC systems may enable cloud computing capabilities and an IT service environment at the edge of the cellular network. By running applications and performing related processing tasks closer to the cellular customer, network congestion may be reduced and applications may perform better. 
     For further explanation,  FIG. 3D  illustrates an exemplary computing device  350  that may be specifically configured to perform one or more of the processes described herein. As shown in  FIG. 3D , computing device  350  may include a communication interface  352 , a processor  354 , a storage device  356 , and an input/output (“I/O”) module  358  communicatively connected one to another via a communication infrastructure  360 . While an exemplary computing device  350  is shown in  FIG. 3D , the components illustrated in  FIG. 3D  are not intended to be limiting. Additional or alternative components may be used in other embodiments. Components of computing device  350  shown in  FIG. 3D  will now be described in additional detail. 
     Communication interface  352  may be configured to communicate with one or more computing devices. Examples of communication interface  352  include, without limitation, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, an audio/video connection, and any other suitable interface. 
     Processor  354  generally represents any type or form of processing unit capable of processing data and/or interpreting, executing, and/or directing execution of one or more of the instructions, processes, and/or operations described herein. Processor  354  may perform operations by executing computer-executable instructions  362  (e.g., an application, software, code, and/or other executable data instance) stored in storage device  356 . 
     Storage device  356  may include one or more data storage media, devices, or configurations and may employ any type, form, and combination of data storage media and/or device. For example, storage device  356  may include, but is not limited to, any combination of the non-volatile media and/or volatile media described herein. Electronic data, including data described herein, may be temporarily and/or permanently stored in storage device  356 . For example, data representative of computer-executable instructions  362  configured to direct processor  354  to perform any of the operations described herein may be stored within storage device  356 . In some examples, data may be arranged in one or more databases residing within storage device  356 . 
     I/O module  358  may include one or more I/O modules configured to receive user input and provide user output. I/O module  358  may include any hardware, firmware, software, or combination thereof supportive of input and output capabilities. For example, I/O module  358  may include hardware and/or software for capturing user input, including, but not limited to, a keyboard or keypad, a touchscreen component (e.g., touchscreen display), a receiver (e.g., an RF or infrared receiver), motion sensors, and/or one or more input buttons. 
     I/O module  358  may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers. In certain embodiments, I/O module  358  is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces and/or any other graphical content as may serve a particular implementation. In some examples, any of the systems, computing devices, and/or other components described herein may be implemented by computing device  350 . 
     In some examples, a non-transitory computer-readable medium storing computer-readable instructions may be provided in accordance with the principles described herein. The instructions, when executed by a processor of a computing device, may direct the processor and/or computing device to perform one or more operations, including one or more of the operations described herein. Such instructions may be stored and/or transmitted using any of a variety of known computer-readable media. 
     A non-transitory computer-readable medium as referred to herein may include any non-transitory storage medium that participates in providing data (e.g., instructions) that may be read and/or executed by a computing device (e.g., by a processor of a computing device). For example, a non-transitory computer-readable medium may include, but is not limited to, any combination of non-volatile storage media and/or volatile storage media. Exemplary non-volatile storage media include, but are not limited to, read-only memory, flash memory, a solid-state drive, a magnetic storage device (e.g. a hard disk, a floppy disk, magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and an optical disc (e.g., a compact disc, a digital video disc, a Blu-ray disc, etc.). Exemplary volatile storage media include, but are not limited to, RAM (e.g., dynamic RAM). 
     Advantages and features of the present disclosure can be further described by the following statements: 
     1. A system comprising: a memory storing instructions; and a processor communicatively coupled to the memory and configured to execute the instructions to: determine, based on a state of snapshots in a data storage system and a set of rules each defining a snapshot capture schedule and a snapshot retention schedule, a rule in the set of rules to use to take the snapshot; and capture the snapshot of the data structure based on the rule, the capturing of the snapshot including setting a retention period of the snapshot based on the rule. 
     2. The system of statement  1 , wherein the processor is configured to execute the instructions to: detect that the retention period of the snapshot has expired; and eradicate the snapshot from the data storage system based on the detection that the retention period of the snapshot has expired. 
     3. The system of any of the preceding statements, wherein the snapshot comprises a policy-managed snapshot that is immutable after the capturing of the snapshot and until the eradicating of the snapshot. 
     4. The system of any of the preceding statements, wherein the detecting that the retention period of the snapshot has expired comprises determining that an uptick counter has reached or exceeded an uptick value that corresponds to an expiration of the retention period. 
     5. The system of any of the preceding statements, wherein the set of rules comprises: a first rule defining a first snapshot capture schedule indicating a first frequency at which to take snapshots; and a second rule defining a second snapshot capture schedule indicating a second frequency at which to take snapshots, the second frequency different from the first frequency. 
     6. The system of any of the preceding statements, wherein: 
     the first rule defines a first snapshot retention schedule indicating a first retention period for which to retain snapshots; the second rule defines a second snapshot retention schedule indicating a second retention period for which to retain snapshots, the second retention period different from the first retention period. 
     7. The system of any of the preceding statements, wherein the determining comprises analyzing the state of snapshots within one or more lookback periods. 
     8. The system of any of the preceding statements, wherein the one or more lookback periods comprise a different lookback period for each rule in the set of rules. 
     9. The system of any of the preceding statements, wherein each lookback period is a snapshot capture period defined by each corresponding rule minus an offset time. 
     10. The system of any of the preceding statements, wherein the processor is configured to execute the instructions to: detect a change to the snapshot before an expiration of the retention period of the snapshot; and capture, in response to the change to the snapshot before the expiration of the retention period of the snapshot, an additional snapshot of the data structure based on the same rule used to capture the snapshot. 
     11. The system of any of the preceding statements, wherein the processor is configured to execute the instructions to: provide a user interface for use by a user of the data storage system to define the set of rules; receive, by way of the user interface, user input defining the set of rules; and provide, for display by way of the user interface, a preview timeline of scheduled captures and eradications of snapshots based on the set of rules. 
     12. The system of any of the preceding statements, wherein: the data storage system comprises a file storage system; the data structure comprises a directory of the file storage system; and the capturing the snapshot of the data structure comprises directing the file storage system to capture the snapshot of the data structure. 
     13. The system of any of the preceding statements, wherein the directing the file storage system to capture the snapshot of the data structure comprises: adding the snapshot to a batch of pending snapshots; and directing the file storage system to capture the pending snapshots in the batch of pending snapshots. 
     14. The system of any of the preceding statements, wherein the system is implemented in middleware configured to use a binary protocol stream to communicate and synchronize with the data storage system. 
     15. A data storage system comprising: a memory storing instructions; and a processor communicatively coupled to the memory and configured to execute the instructions to: determine, based on a state of snapshots within one or more lookback periods, a rule to use to take a snapshot; and capture the snapshot based on the rule, the capturing of the snapshot including setting a retention period of the snapshot based on the rule. 
     16. The method of any of the preceding statements, wherein: the rule is included in a set of rules defining a data protection plan for the data storage system; and the one or more lookback periods comprise a set of lookback periods that includes a different lookback period for each rule in the set of rules. 
     17. The method of any of the preceding statements, wherein each lookback period is a snapshot capture period defined by each corresponding rule minus an offset time. 
     18. A method comprising: determining, by a processor and based on a state of snapshots in a data storage system and a set of rules each defining a snapshot capture schedule and a snapshot retention schedule, a rule in the set of rules to use to take the snapshot; and capturing, by the processor, the snapshot of the data structure based on the rule, the capturing of the snapshot including setting a retention period of the snapshot based on the rule. 
     19. The method of any of the preceding statements, further comprising: detecting, by the processor, that the retention period of the snapshot has expired; and eradicating, by the processor, the snapshot from the data storage system based on the detecting that the retention period of the snapshot has expired. 
     20. The method of any of the preceding statements, wherein: the data storage system comprises a file storage system; the data structure comprises a directory of the file storage system; and the snapshot comprises a policy-managed snapshot that is immutable after the capturing of the snapshot and until the eradicating of the snapshot. 
     One or more embodiments may be described herein with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claims. Further, the boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality. 
     To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claims. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof. 
     While particular combinations of various functions and features of the one or more embodiments are expressly described herein, other combinations of these features and functions are likewise possible. The present disclosure is not limited by the particular examples disclosed herein and expressly incorporates these other combinations. 
     In accordance with certain embodiments of the present disclosure, a storage system may provide unified data storage on block containers and, using the unified storage on block containers, may provide one or more data storage services such as block, file, object, and/or database services. To this end, the storage system may leverage block containers and one or more resources associated with block containers such that a data storage service provided by the storage system benefits from and/or has access to block containers and resources associated with block containers. In certain examples, this may allow block containers that are optimized for implementation using all-flash data storage to be used to provide one or more data storage services that are similarly optimized for implementation using all-flash data storage. The storage system may provide and/or facilitate efficient operations on block containers and container data, such as efficient writing, reading, extending, thin-provisioning, deleting, range-copying, mapping, unmapping, snapshotting, cloning, replicating, compressing, deduplicating, garbage collecting, etc. 
       FIGS. 4A-4B  illustrate an example of a data storage system  400  that includes a block container system  402  and a container utilization system  404  communicatively coupled to one another. Block container system  402  and container utilization system  404  may be communicatively coupled using any suitable data communication technologies. 
     Block container system  402  may provide block containers  405  and resources  406  associated with block containers  405 . Block containers  405  may represent a linear address space of blocks where block container system  402  may quickly and dynamically create and delete block containers. Block containers  405  may be thin provisioned with a block range deallocate capability. The block address space may be any suitable size, including very large (e.g., a 64-bit address space of bytes or blocks) in some examples. Implementations of block containers  405  may support operations and/or sharing or stored content within and between block containers  405 , such as operations or sharing of stored content across block containers of different types (e.g., across block containers of different forms used for different types of storage systems). Examples of such operations or sharing of stored content include, without limitation, snapshots, clones, checkpoints, replication, deduplication, compression, encryption, and virtual copy by reference operations for entire block containers (e.g., cloning by reference the content of one block container to another) as well as for ranges of blocks within and between block containers. Block containers  405  may be tagged, grouped, and/or named to allow operations or mechanisms such as snapshots, clones, checkpoints, and replication to operate consistently and atomically on various types of sets of block containers  405  to form or replace matching sets of block containers  405 . Such features may facilitate grouping of individual block containers  405  or sets of block containers  405  into groups. 
     An example of such a group may be referred to as a “pod.” A pod may represent a dataset along with one or more storage systems that store the dataset. A pod may have a name and an identifier. Storage systems may be added to and removed from a pod. When a storage system is added to a pod, the pod&#39;s dataset may be copied to that storage system and then kept up to data as the dataset is modified. When a storage system is removed from a pod, the pod&#39;s dataset is no longer kept up to date on the storage system. Any storage system that is active for a pod can receive and process requests to modify or read the pod. A pod may also be a unit of administration that represents a collection of block containers  405 , volumes, file systems, object/analytic stores, snapshots, and other administrative entities where making administrative changes on any one storage system is automatically reflected to all active storage systems associated with the pod. In the context of storage systems described herein, a pod may operate as a namespace for some set of objects (for example, block containers or snapshots or clones of block containers) that can be operated on as a set, such that additions or removals of a set of objects (such as block containers) from the pod causes future operations to operate on the adjusted collection of these objects. If a pod is replicated, such as through symmetric synchronous replication, between storage systems, then each storage system which is operating normally against the pod will store the same collection of these objects with the same names and other identify or tag metadata. 
     In certain examples, implementations of block containers  405  may optimize data storage system  400  for flash data storage (e.g., all-flash data storage). For example, block container system  402  may be configured to generally gather a collection of updates which will be organized into segments (e.g., medium-sized segments) that are written and managed in such a way that the content and the capacity held by the stored segments are changed or reused through a garbage collection process. Segments may be any suitable size, such as on the order of megabytes, and may be sized optimally around flash erase blocks or sized optimally for erasure coded segments written as shards across several flash-based storage devices which are organized as erase blocks or to be a reasonably optimal size for desired throughput in writing and reading whole data structures in a data store operating as a bulk backend for data storage system  400  and/or a data storage service provided by data storage system  400  (e.g., writing and reading whole objects in an object store operating as a build backend for data storage system  400 ). Such segments can work with flash-based data storage that exposes erase blocks directly or through some scheme such as zoned drives. Such a scheme can also be used to organize data to be written (and eventually garbage collected) to non-flash-based zoned drives or to medium-sized objects in a typical type of object store that best supports objects which are written and eventually deleted without ever having been updated in place. 
     In certain embodiments, block container system  402  may include and/or may be specifically designed to use all-flash data storage for block-based data storage. To this end, block container system  402  may be a flash block-based storage system that is optimized for flash block-based storage and that preserves data integrity, provides consistent low latency and high performance, and maximizes physical media endurance. 
     Resources  406  associated with block containers  405  may include any components of block container system  402  configured to create, delete, modify, thin provision, allocate, deallocate, append, or otherwise operate on block containers  405  and/or content of block containers  405 . Resources  406  may be implemented in any suitable way, including as hardware and/or software components (e.g., as computer-implemented instructions executable by a processor to perform operations on block containers  405 ). Resources  406  may include any of the storage system resources described herein, including any of storage resources  308 , communications resources  310 , processing resources  312 , software resources  314 , and other resources described above. Examples of resources  406  of block container system  402  include, without limitation, data reduction resources (e.g., pattern removal, deduplication, and compression resources), metadata resources (e.g., metadata such as one or more maps that track relationships between logical block addresses and physical media addresses), data structure resources (e.g., graphs of structures called logical extents), data replication resources (e.g., snapshot, clone, extended copy, asynchronous replication, synchronous replication resources), storage reclamation resources (e.g., garbage collection resources), lookaside data structure resources for tracking operations on data for use in implementing or completing implementation of changes in a time-shifted manner (e.g., by a background process at a suitable time), foreground or inline data processing resources (e.g., foreground or inline data reduction as part of a data write process), and background processing resources (e.g., background data reduction, storage reclamation, etc. applied to logically stored data that remains unaltered for a threshold length of time). Examples of resources  406  of block container system  402  may include components and/or processes configured to perform operations such as writing, reading, extending, thin-provisioning, deleting, range-copying, mapping, unmapping, snapshotting, cloning, replicating, compressing, deduplicating, and garbage collecting block containers  405  and/or content of block containers  405 . 
     Block containers  405  may be configured to store and address data in blocks of any suitable size and configuration. Accordingly, block containers system  402  may utilize block containers  405  for multiple types of uses. In certain implementations, block containers  405  may be used to represent volumes, which may function to a client host as individual data storage drives accessed through SCSI, NVMe, or some other block protocol (e.g., mounted drive volumes accessible by an operating system). In certain implementations, block containers  405  may additionally or alternatively include or represent fixed or variable size blocks of data that each contain a number of sectors of data (e.g., zero or more 512-byte sectors of data). 
     Block container system  402  may include or implement any of the storage systems or features of the storage systems described above. In certain embodiments, block container system  402  may include a virtually mapped allocate-on-write and/or copy-on-write block-based storage system. 
     Block containers  405  may be implemented in any way suitable to provide one or more of the features of block container system  402  described herein. Examples of architectures for implementing block containers  405  will now be described. The examples are illustrative and not limiting. Additional or alternative architectures for implementing block containers  405  may be used in other examples. 
     In certain examples, block containers  405  may be implemented using a linear table of vectors to blocks or content-addressable stores. In certain examples, block containers  405  may be implemented using a two-level vector of block references where blocks themselves are compressed and written into garbage collection segments. In certain implementations, the two-level vector may use copy-on-write of a more granular vector level. In certain examples, block containers  405  may be implemented using hash references (e.g., hash tables) to blocks or content-addressable stores. In certain examples, block containers  405  may be implemented using B-trees or similar data structures that reference blocks or content-addressable stores. 
     In certain examples, block containers  405  may be implemented using graphs of structures called logical extents. A logical extent may include a list of pointers to logical block addresses, to other logical extents, or to a combination of logical block addresses and other logical extents. Logical extents may be linked together to form a data structure such as graph of logical extent nodes that represents relationships between data. Any suitable structure of logical extents may be used, such as an acyclic graph of logical extents. In certain examples, block container system  402  may use a directed acyclic graph (DAG′) or a balanced directed acyclic graph (‘B-DAG’) of logical extents. 
     In certain examples, logical extents may be categorized into two types of logical extents: leaf logical extents that reference some amount of stored data in some way (e.g., by including or pointing to logical addresses mapped to physical media locations of data) and composite logical extents that reference other leaf or composite logical extents. 
     A leaf extent can reference data in a variety of ways. It can point directly to a single range of stored data (e.g., 64 kilobytes of data), or it can be a collection of references to stored data (e.g., a 1 megabyte “range” of content that maps some number of virtual blocks associated with the range to physically stored blocks). In the latter case, these blocks may be referenced using some identity, and some blocks within the range of the extent may not be mapped to anything. Also in the latter case, these block references need not be unique, allowing multiple mappings from virtual blocks within some number of logical extents within and across some number of block containers (e.g., block containers  405 ) to map to the same physically stored blocks. Instead of stored block references, a logical extent could encode simple patterns. For example, a block that is a string of identical bytes could simply encode that the block is a repeated pattern of identical bytes. 
     A composite logical extent can be a logical range of content with some virtual size, which comprises a plurality of maps that each map from a subrange of the composite logical extent logical range of content to an underlying leaf or composite logical extent. Transforming a request related to content for a composite logical extent, then, involves taking the content range for the request within the context of the composite logical extent, determining which underlying leaf or composite logical extents that request maps to, and transforming the request to apply to an appropriate range of content within those underlying leaf or composite logical extents. 
     In certain implementations of block container system  402 , block containers can be defined as logical extents. Thus, block containers can be organized using the logical extent model. In certain examples, a graph of logical extents may include a root node associated with a block container in block container system  402 . The root node may point to one or more other nodes that point to other nodes and/or logical addresses mapped to physical media locations at which data associated with the block container is stored. 
     Depending on implementation, leaf or composite logical extents could be referenced from a plurality of other composite logical extents, effectively allowing inexpensive duplication of larger collections of content within and across block containers. Thus, logical extents can be arranged essentially within an acyclic graph of references, each ending in leaf logical extents. This can be used to make copies of block containers, to make snapshots of block containers, or as part of supporting virtual range copies within and between block containers as part of an extended copy operation or similar types of operations. 
     An implementation may provide each logical extent with an identity which can be used to name it. This simplifies referencing, since the references within composite logical extents become lists comprising logical extent identities and a logical subrange corresponding to each such logical extent identity. Within logical extents, each stored data block reference may also be based on some identity used to name it. 
     To support these duplicated uses of extents, logical extents may be configured as copy-on-write logical extents. When a data modifying operation affects a copy-on-write leaf or composite logical extent, the logical extent is copied, with the copy being a new reference and possibly having a new identity (depending on implementation). The copy retains all references or identities related to underlying leaf or composite logical extents, but with whatever modifications result from the modifying operation. For example, a write request, a write same request, an extended write read request, an extended write request, or a compare and write request may store new blocks in the storage system (or use deduplication techniques to identify existing stored blocks), resulting in modifying the corresponding leaf logical extents to reference or store identities to a new set of blocks, possibly replacing references and stored identities for a previous set of blocks. Alternately, an un-map request may modify a leaf logical extent to remove one or more block references. In both types of cases, a leaf logical extent is modified. If the leaf logical extent is a copy-on-write logical extent, then a new leaf logical extent will be created that is formed by copying unaffected block references from the old extent and then replacing or removing block references based on the modifying operation. 
     A composite logical extent that was used to locate the leaf logical extent may then be modified to store the new leaf logical extent reference or identity associated with the copied and modified leaf logical extent as a replacement for the previous leaf logical extent. If that composite logical extent is a copy-on-write logical extent, then a new composite logical extent is created as a new reference or with a new identity, and any unaffected references or identities to its underlying logical extents are copied to that new composite logical extent, with the previous leaf logical extent reference or identity being replaced with the new leaf logical extent reference or identity. 
     This process continues further backward from referenced extent to referencing composite extent, based on the search path through the acyclic graph used to process the modifying operation, with all copy-on-write logical extents being copied, modified, and replaced. 
     These copied leaf and composite logical extents can then drop the characteristic of being copy on write, so that further modifications do not result in a copy. For example, the first time some underlying logical extent within a copy-on-write “parent” composite extent is modified, that underlying logical extent may be copied and modified, with the copy having a new identity which is then written into a copied and replaced instance of the parent composite logical extent. But, a second time some other underlying logical extent is copied and modified and with that other underlying logical extent copy&#39;s new identity being written to the parent composite logical extent, the parent can then be modified in place with no further copy and replace necessary on behalf of references to the parent composite logical extent. 
     Modifying operations to new regions of a block container or of a composite logical extent for which there is no current leaf logical extent may create a new leaf logical extent to store the results of those modifications. If that new logical extent is to be referenced from an existing copy-on-write composite logical extent, then that existing copy-on-write composite logical extent will be modified to reference the new logical extent, resulting in another copy, modify, and replace sequence of operations similar to the sequence for modifying an existing leaf logical extent. 
     If a parent composite logical extent cannot be grown large enough (based on implementation) to cover an address range associated with new leaf logical extents to be created for a new modifying operation, then the parent composite logical extent may be copied into two or more new composite logical extents which are then referenced from a single “grandparent” composite logical extent which yet again is a new reference or a new identity. If that grandparent logical extent is itself found through another composite logical extent that is a copy-on-write logical extent, then that other composite logical extent will be copied and modified and replaced in a similar way as described in previous paragraphs. 
     This copy-on-write model can be used as part of implementing snapshots, block container copies, and virtual block container address range copies within a storage system implementation based on these directed acyclic graphs of logical extents. To make a snapshot as a read-only copy of an otherwise writable block container, a graph of logical extents associated with the block container is marked copy-on-write and references to the original composite logical extents are retained by the snapshot. Modifying operations to the block container will then make logical extent copies as needed, resulting in the block container storing the results of those modifying operations and the snapshots retaining the original content. Block container copies are similar, except that both the original block container and the copied block container can modify content resulting in their own copied logical extent graphs and subgraphs. 
     Virtual block container address range copies can operate either by copying block references within and between leaf logical extents (which does not itself involve using copy-on-write techniques unless changes to block references modify copy-on-write leaf logical extents). Alternately, virtual block container address range copies can duplicate references to leaf or composite logical extents. This works well for block container address range copies of larger address ranges. And, this is one way the graphs can become directed acyclic graphs of references rather than merely reference trees. Copy-on-write techniques associated with duplicated logical extent references can be used to ensure that modifying operations to the source or target of a virtual address range copy will result in the creation of new logical extents to store those modifications without affecting the target or the source that share the same logical extent immediately after the block container address range copy operation. 
     Logical extents, such as described above, are an example of an architecture for implementing block containers  405 . Another example includes single-instance stores, where blocks are stored in association with an index derived from a secure hash fingerprint of their content and where a block container would effectively be an array of references that is updated whenever a block is written. A simple two-level logical extent model, where one level represents a vector of references to individual leaf logical extents represents a simpler version of the logical extent model where the leaf logical extents can reference a deduplicated block store or a content addressable block store. Whatever the model, the relationship between data stored into block containers  405  at particular logical block addresses and bulk storage should be dynamic to ensure that new data is written into new blocks that are arranged within medium-sized segments, and organized around garbage collection processes that can move blocks that are still referenced while deleting blocks that are no longer referenced in order to either reclaim space from segments that contain some no longer referenced blocks or, particularly in the case of flash memory, in order to address flash memory durability issues. 
     Block container system  402  may be configured to present handles to block containers  405 . The handles may include any suitable data identifying block containers  405  such as pointers to block containers  405  (e.g., pointers to root nodes of block containers  405 ). The handles may be used by container utilization system  404  to operate on and/or interact with block containers  405 , such as by writing content to and/or reading content from block containers  405 . 
     Container utilization system  404  may be configured to utilize block containers  405  to store and manage content for one or more data storage services provided by a unified data storage system. As an example, for a block storage service, a block volume may be implemented on a single block container  405  (a block container representing a volume). As another example, for a file storage service, individual files, directories, file system data, and/or metadata may be implemented on individual block containers  405 . As another example, for an object storage service, individual objects and metadata may be implemented on individual block containers  405 . As another example, for a database, block containers  405  may be allocated to store individual redo logs, archive logs, table spaces, blobs (binary large objects), data configuration data, and/or metadata. 
     To this end, container utilization system  404  may be configured to use block containers  405  of block container system  402 , as well as resources  406  of block container system  402  in some examples, to provide one or more data stores for storing and managing content for one or more data storage services. For example, container utilization system  404  may provide a data store  408  that utilizes block containers  405  to represent data stored in data store  408 . Accordingly, data store  408  may be referred to as a container-based data store  408  for storing and managing content for a data storage service. Data store  408  may operate as a bulk backend data store for one or more data storage services. 
     Container utilization system  404  may be configured to facilitate use of block containers  405  to represent data for any suitable number and/or type of data storage services. To this end, container utilization system  404  may provide and maintain any suitable number and types of data stores for storing content for any suitable number and/or type of data storage services. In at least this regard, data storage system  400  may be a unified storage system that supports various types of data storage services and/or data storage protocols. For example, container utilization system  404  may provide one or more data stores  408  for storing content for block, file, object, and/or database storage services. 
     Block containers  405  of block container system  402  may be adapted and/or used differently by container utilization system  404  for various data storage services. For example, certain block containers  405  may be used to represent content for a first storage service (e.g., a block storage service), certain block containers  405  may be used to represent content for a second storage service different from the first storage service (e.g., a file storage service), etc. 
     In certain embodiments in which block containers  405  are implemented using graphs of logical extents as described above, container utilization system  404  may be configured to use graphs of logical extents to represent and manage data in data store  408 . For example, a data instance for a data storage service may be represented as a block container defined by a graph of logical extents that point to and/or include data for the data instance. Container utilization system  404  may associate the data instance with the block container defined by the graph of logical extents, such as by associating an identifier for the data instance with a root node of the graph of logical extents. 
       FIG. 5  illustrates an example of a block container  500  used to represent a data instance in accordance with some embodiments. As shown in  FIG. 5 , block container  500  may include a graph (e.g., an acyclic graph such as a directed acyclic graph or a balanced directed acyclic graph) of logical extent nodes  502 - 1  through  502 - 3  (collectively “logical extent nodes  502 ,” “logical extents  502 ,” or “nodes  502 ”). In the illustrated graph, node  502 - 1  is a root node, node  502 - 2  is a child node of node  502 - 1 , and node  503 - 3  is a child node of node  502 - 2 . 
     The graph of logical extents is associated with and represents a data instance. In the illustrated example, a data instance identifier (data ID)  506  is mapped to root node  502 - 1  to associate a data instance having the data ID  506  to the root node  502 - 1  and consequently to the graph of logical extents  502 . Data ID  506  may be any suitable unique identifier and may be produced in any suitable way. For example, container utilization system  404  may generate or receive data ID  506 . Container utilization system  404  may map the data ID  506  to root node  502 - 1  in any suitable way. 
     Each of nodes  502  includes a list of one or more pointers to other nodes  502  and/or to logical addresses mapped to physical media locations at which data associated with the data object is stored. For example, root node  502 - 1  may include a pointer to node  502 - 2 , which may include a pointer to node  502 - 3 . Node  502 - 3  may include pointers to logical addresses such as logical block addresses that are mapped to locations in physical media at which data for the object is stored. In  FIG. 5 , mediums  504 - 1  through  504 - 3  (collectively “mediums  504 ”) may represent the logical addresses (in a linear address space) and/or locations in physical media at which data for the data instance is stored. Accordingly, nodes  502 - 1  and  502 - 2  may be composite logical extents, and node  502 - 3  may be a leaf logical extent. 
     While  FIG. 5  depicts a data ID  506  for a data instance being mapped to root node  502 - 1 , this is illustrative of certain embodiments. A data instance may be associated with a graph of logical extents in any suitable way, including by directly mapping the data instance to any other suitable internal handle for supporting virtualized thin provisioning of block containers  405  and/or blocks in block container system  402 . Moreover, the use of a graph of logical extents to represent a block container  405  is illustrative only. Block containers  405  may be represented using any suitable architecture, including any of the other illustrative architectures described herein. 
     The use of block containers  405  to represent data instances in data store  408  may provide flexibility for the storage and management of the data instances. For example, for certain data storage services, data instances can be any size and can be resized by reconfiguring the block containers  405  representing the data instances. As another example, additional resources of block container system  402  may be used, such as by applying the resources to the block containers  405  to perform one or more operations on the data instances. To illustrate, data reduction resources, data replication resources (e.g., snapshotting), and/or storage reclamation resources of block container system  402  may be applied to the data instances represented by block containers  405 . Such applications may provide instantaneous and/or efficient snapshotting of the data instances in data store  408 , reducing of data by using multiple block containers  405  to point to the same stored content that are associated with multiple data instances, and garbage collecting to reclaim storage in data store  408 . 
     Block containers  405  of block container system  402  may be adapted for representing and managing data in data store  408 . For example, an architecture used to represent a block container  405  may be adapted to reduce or minimize overhead that is associated with the block container  405 . To illustrate, a block container  405  may be associated with a volume in a block storage service such that the block container  405  is tied to an overhead associated with the volume. The block container  405  or another block container  405  may be adapted by freeing it from the volume and/or at least some of the overhead associated with the volume. For example, overhead such as metadata overhead, administrative overhead, visual overhead, representation overhead associated with presented, user managed structures, and/or overhead associated with host exports may be reduced or for some types of overhead eliminated from the block container system  402 . For example, block containers  405  may be presented to and/or otherwise used by one or more storage services without being presented to or in any way directly used by users of the storage services and/or without being exported or otherwise exposed externally on an individual basis. Such configurations may allow block containers  405  to have minimal overhead in block container system  402 , and in some cases to have none of one or more of the types of overheads mentioned above. For example, a storage service such as a file storage service may present a file system as a whole, as an exported and user administered entity. These features may allow a data instance to be represented with a block container  405  that has substantially less overhead in comparison with, say, a set of block devices which are implemented using similar internal structure as the described block container. Accordingly, for the same finite physical storage resources, container utilization system  404  may store and manage many more data instances represented with block containers  405  compared to the number of volumes that can be represented in block container system  402 . In certain examples, for instance, only tens of thousands of volumes may be practically represented using full administrative load volumes implemented in the style of block containers  405  compared to practical support for billions of data instances being represented using adapted, low administrative load block containers  405 . 
     Container utilization system  404  may be configured to perform one or more operations to provide data store  408  and to represent and manage data in data store  408  using block containers  405  and resources  406  of block container system  402 . Such operations may include, without limitation, writing data, reading data, creating a data instance, setting a length of a data instance, retrieving information about data, deleting data, snapshotting data, appending data, reducing data, replicating data, and reclaiming storage in data store  408 . In certain examples, garbage collection resources and/or other resources of block container system  402  may be scaled to handle large numbers of data instances represented with block containers  405 . 
     In certain examples, block containers  405  can provide identifiers formed of multiple parts that may include, for example, a namespace part and a unique identifier within the context of the namespace, where different parts of the identifiers might be provided by varying combinations of the layer implementing the block containers  405  and the layer requesting that block containers  405  be created. Snapshots, clones, checkpoints, and replicas can form new block container names such that some parts are retained while others are adjusted in a common way, such as by retaining some unique identifier associated with a particular block container while including a common substring representing the snapshot, clone, checkpoint, or replica. So, for example, given a file storage system built from block containers  405  named using a combination of a file system name or identifier and a block container identifier that is unique relative to the file system, when making a clone of the file storage system&#39;s files, directories, and other data and metadata, the cloned block containers  405  can retain their file system relative unique identifiers while adjusting the file storage system identifier part to be an identifier for the file storage system clone. Then, file storage system or other storage service logic can be pointed at the cloned block containers  405  based on the name pattern of the file storage system clone while reusing the unique identifier parts of the block container names to match the block containers  405  to the logical components within the file storage system. 
     Alternately, or additionally, block containers  405  can be grouped in various ways such that block containers  405  have names (including names that are simple, dynamically assigned integers) within some group. In such a case, a file storage system or other storage service may be built to make use of one or more groups, such that making a snapshot or clone of a group results in a new group that retains the block container names but has a different group name or identifier. In this case, storage service logic can be pointed at a group instead of utilizing some other naming pattern of block containers  405 . 
     In certain examples, such groups may be based on pods, or for a pod-based storage system, groups or other types of block container collections can be contained within pods. Pods may provide a convenient scheme for additional forms of snapshots, clones, replication schemes, symmetric synchronous replication, continuous data protection, and more. 
     Container utilization system  404  may provide an application program interface (API)  410  as shown in  FIG. 4B . API  410  may include a set of defined operations that may be performed by container utilization system  404  when the operations are called. The set of operations may include and/or support any of the operations of container utilization system  404  described herein. As shown in  FIG. 4B , API  410  may be communicatively coupled to data store  408  and block containers  405 . Accordingly, API  410  may perform operations on block containers  405  of block container system  402 . In certain examples, API  410  may perform operations on block containers  405  by invoking and applying one or more resources  406  (e.g., defined functions) of block container system  402  to perform operations described herein. API  410  may also perform operations on data store  408  and data stored in data store  408 . 
     API  410  may be exposed to and used by one or more data storage services. In such a configuration, the data storage services may leverage and benefit from one or more features of data storage system  400 . For example, the data storage services may directly leverage and benefit from data storage provided by container utilization system  404  and indirectly leverage and benefit from block containers  405  and resources  406  of block container system  402  that are used by container utilization system  404 . 
       FIG. 6A  illustrates an example of a data storage system  600  that includes a data storage service  602  configured to interface with container utilization system  404  by way of API  410 . Data storage service  602  may be implemented in any suitable way (e.g., as a data storage system, as an engine, as a virtual data system, etc.) and may interface with container utilization system  404  in any suitable way, including by issuing calls to and receiving responses to the calls from API  410  of container utilization system  404 . 
     Data storage service  602  may be any suitable type of data storage service, such as a file storage service that uses one or more file storage protocols, an object storage service that uses one or more object storage protocols, a block storage service that uses one or more block storage protocols, a database service that uses one or more database protocols, etc. In certain examples, data storage service  602  is a data storage service  602  that is not a block storage service. 
     Data storage system  400  and/or data storage system  600  may provide data storage service  602  with access to resources of block container system  402  and container utilization system  404  (e.g., through API  410 ) such that data storage service  602  may be built on block containers  405 . For example, data storage service  602  may store and manage content of data storage service  602  in data store  408 , and the content may be stored and managed using block containers  405  as described herein. 
     An example in which data storage service  602  is a file storage service will now be described. The example is illustrative. A file storage service may be implemented in other ways in other examples. Data storage service  602  may be a different type of data storage service in yet other examples. 
     In certain implementations, data storage service  602  may include a virtual file system (VFS) configured to manage file data. For example, the VFS may manage metadata that represents information about data files and how the data files map to data instances such as block containers stored in data store  408 . In certain embodiments, the VFS may employ data structures called envelopes in which metadata about the data files and mappings of the data files to block containers is maintained. An envelope may include information about one or more data files. Envelopes may include any suitable information about data files and corresponding block containers, including, file identifiers, block container identifiers, container store map information (e.g., info about relationships between file identifiers and block container identifiers), snapshot info for the data (indicating older block container versions), root node identifiers for root nodes of graphs representing block containers in container-based data store  408 , size information for files, block containers, and/or graphs of logical extents, statistics for the data, log information for the data, and the like. In some examples, envelopes may be directories plus caches of metadata about files contained in the directories. Data storage service  602  may be configured to provide any information about data files, including block container identifiers and/or other information represented in envelopes, to API  410  in conjunction with calling functions of API  410 . Data storage service  602  may also be configured to receive information about data files, including block container identifiers and/or other information to be represented in envelopes (e.g., logical size of a file, logical space used for the file, etc.), from API  410  in conjunction with responses from called functions of API  410 . 
     In certain implementations, data storage service  602  may be configured to generate and provide block container identifiers for use by container utilization system  404 . Container utilization system  404  may be configured to use the block container identifiers as local identifiers within container utilization system  404  or may be configured to map the block container identifiers to local identifiers generated and used within container utilization system  404 . 
     In certain embodiments, data storage service  602  and container utilization system  404  may be configured to represent each data file or envelope as a block container in data store  408 . Data storage service  602  may be configured to cache files and push cached block containers to container utilization system  404  in batches for writing to data store  408 . Data store  408 , in turn, may persist block containers and associated versions (e.g., snapshots) and make the block containers available for fast access by data storage service  602 . 
     Container utilization system  404  may be configured to provide transaction semantics for use by data storage service  602 . In certain embodiments, the transaction semantics may allow, for at least some classes of operations, data storage service  602  to issue multiple updates to a file and request that all or none of the updates be made to the block container representing the file. Container utilization system  404  may be configured to implement all or none of the updates and return either new or old data to data storage service  602 . 
     In certain implementations, container utilization system  404  and data storage service  602  may be configured to support multi-container transactions. For example, container utilization system  404  may use one or more resources  406  of block container system  402  to provide an array of parallel transaction queues (e.g.,  32  buffers) that can be written to independently for increased throughput. The number of parallel transaction queues written to may be maximized to increase throughput. 
     To illustrate the use of such parallel transaction queues, a transactional update may include data for multiple metadata elements and/or operations (e.g., create a block container, create another block container, append to a block container, etc.). Container utilization system  404  may be configured to perform everything associated with such a transactional update all together or not at all. Accordingly, everything in the transactional update may be made visible to a host at the same time. 
     Data storage service  602  may be configured to provide file storage services (e.g., to a host server that is not shown in  FIG. 6A ). To this end, data storage service  602  may be configured to support any file-based storage protocols, including for example, NFS, SMB, and/or CIFS. One or more such protocols may be used by a host server, for example, to interface with the VFS. 
     In the case of a storage system providing a file service, such as through NFS or SMB, container utilization system  404  makes it very simple for the file service layer to concentrate on protocol handling and file service semantics without being concerned about layout out files on the storage, while benefiting from the rich semantics of block containers  405 . For example, file stores get the benefit of deduplication and compression without having to implement those features separately, will also benefit from all the other block container or pod services such as snapshots, clones and various types, forms, and methods of replication, and may be able to leverage symmetric synchronous replication to provide symmetrically accessible mirrored files in a simple fashion. Individual files can be cloned easily by virtually copying one block container to another as part of creating a new file. A file can be re-cloned by virtually copying the block container associated with the first file to the block container associated with the second container. This can, for example, make it very fast and space efficient to copy a collection of virtual machine images stored as files, or for storing Docker- and Kubernetes-style containers which are generally stored as a collection of overlay directories but where cloning operations could be used to eliminate the separate overhead of reading and writing to these overlays during run-time operation of such a container 
     In certain examples, a file system of a file storage service can be versioned by making a sequence of snapshots of the file system&#39;s associated block containers based on naming or grouping rules. If subtrees of a file system are differentiated in a coordinated way such as with attributes of block containers, for example based on various kinds of tags or name components, then a file system can leverage that to get highly performant and flexible subdirectory snapshots through requests to the block container service layer to snapshot block containers with those attributes to create new block containers with a particular set of new attributes to differentiate them from the original block containers. 
     While an example of a file storage service system interfacing with container utilization system  404  has been described above, any suitable data storage service system, including another type of data storage service system, may interface with container utilization system  404 . For example, an object-based storage service system (e.g., an S3 storage service system) may interface with container utilization system  404 . An object store may get the benefit of deduplication and compression without having to implement those features separately, may also benefit from all the other block container or pod services, and may even be able to leverage symmetric synchronous replication to provide symmetrically accessible mirrored objects in a simple fashion. Individual objects can be cloned easily by virtually copying one block container to another as part of creating a new object. An object can be re-cloned by virtually copying the block container associated with the first object to the block container associated with the second container. This can, for example, make it very fast and space efficient to copy a collection of virtual machine images or containers. As another example, a database service system may interface with container utilization system  404 . A database that stores blobs may benefit from deduplication and/or inherit additional block container and pod capabilities. 
     A storage service system that interfaces with container utilization system  404  may be configured to provide data storage services using any suitable data storage protocol or set of protocols. 
     While  FIG. 6A  illustrates one data storage service  602  supported by container utilization system  404  and block container system  402 , these systems may be configured to support any number of data storage services, including data storage services of different types.  FIG. 6B  illustrates an example of a data storage system  604  that includes N number of data storage services  602  (data storage services  602 - 1  through  602 -N) configured to interface with container utilization system  404  by way of API  410 . The plurality of data storage services  602  shown in  FIG. 6B  may include any suitable combination of data storage services (e.g., file, block, object, database, etc.) configured to interface with container utilization system  404  by way of API  410 . 
     Data storage services  602  may be provided with access to resources of block container system  402  and container utilization system  404  (e.g., through API  410 ) such that data storage services  602  may be built on block containers  405 . For example, data storage services  602  may store and manage content of data storage services  602  in one or more data stores (e.g., data store  408 ) maintained by container utilization system  404 , and the content may be stored and managed using block containers  405  as described herein. 
     Container utilization system  404  may function as an intermediary between block container system  402  and one or more data storage services  602 . This may allow different data storage services  602  to easily interface with container utilization system  404  (e.g., by way of API  410 ) in order to benefit from block containers  405  and resources  406  of block container system  402 . 
     In certain examples, block container system  402  and/or container utilization system  404  may be configured to allow space efficient virtual copying or cloning of block ranges or block containers associated with one storage service to be used by another storage service. As example, individual files or sets of files of one file system may be virtually copied to another file system. As another example, individual files or sets of files of a file system may be turned into individual objects or sets of objects in an object store (or vice versa). As another example, a database blob may be virtually copied from a database to make a file or object. In a more complex example, a file contained within a virtual machine&#39;s file system image where the file system image is itself stored, for example, as either a block volume or an individual file on a block container, could be virtually copied to form an independent file or object within a file system or object store that directly utilizes block containers. 
     In certain examples, block container system  402  and/or container utilization system  404  may be configured to provide a variant of this in which multiple identities may be provided for a block container such that different storage service can share a block container, which sharing may include inheriting changes to the content of the block container. For example, a file system and an object store may share a block container. The file system may use a first identifier for the block container, and the object store may use a second identifier for the block container. Changes made to content of a file in the file system may be inherited by a corresponding object in the object store by way of the changes to the content of the file being made in the shared block container. 
     While  FIGS. 4A, 4B, 6A, and 6B  illustrate container utilization system  404  and block container system  402  as separate entities, other configurations may be implemented in other embodiments. As an example, container utilization system  404  and block container system  402  may be flattened into a container storage system configured to operate in any of the ways described herein. This may be accomplished in any suitable way, including by implementing one or more of resources  406  in a container storage system. The resources  406  implemented in the container storage system may include the same resources as may be used in a block storage system and/or may include adaptations of the resources that are configured for use with the container storage system. The reader will appreciate that any other implementation of a container storage system may be configured to operate in any of the ways described herein using any of the illustrative resources described herein and/or other suitable resource. 
       FIG. 7  illustrates an exemplary method  700  for a data storage system. While  FIG. 7  illustrates exemplary operations according to one embodiment, other embodiments may omit, add to, reorder, and/or modify any of the operations shown in  FIG. 7 . One or more of the operations shown in  FIG. 7  may be performed by data storage system  400 , any components included therein, and/or any implementation thereof. 
     In operation  702 , block containers are provided. For example, block container system  402  may provide block containers  405  in any of the ways described herein. 
     In operation  704 , the block containers are used to store content for one or more data storage services. For example, container utilization system  404  may use block containers  405  to store content for one or more data storage services  602  in any of the ways described herein. 
     In operation  706 , the content stored with the block containers is managed. For example, block container system  402  and/or container utilization system  404  may perform one or more operations to manage the content, such as by applying resources  406  of block container system  402  to perform snapshotting, cloning, deduplication, compression, garbage collection, and/or other operations on the content stored with block containers  405 . 
       FIG. 8  illustrates an exemplary method  800  for writing and managing data. While  FIG. 8  illustrates exemplary operations according to one embodiment, other embodiments may omit, add to, reorder, and/or modify any of the operations shown in  FIG. 8 . One or more of the operations shown in  FIG. 8  may be performed by data storage system  400 , any components included therein, and/or any implementation thereof. 
     In operation  802 , a data storage system receives a request to write data. The data storage system may receive the request from any suitable source, such as a data storage service  602 , another data storage system configured to interface with the data storage system, or a host (e.g., a host controller, a host server, etc.). 
     The request may include any information indicating or associated with the request. For example, the request may include data to be written and information about the data, such as a data identifier (a data file identifier, a block container identifier to be used by the data storage system to identify a block container to which the data is written), size information for the data, etc. Data fields included in the request may by defined by a schema used to interface with the data storage system. 
     In operation  804 , the data storage system stores the data to a data store in response to the request received in operation  802 . The data storage system may use one or more block containers to store the data to the data store. 
     Operation  804  may include the data storage system performing one or more processes to store the data to the data store. The processes may use any of the illustrative resources described herein. In certain examples, the storing of the data to the data store may include multi-stage processes such as a frontend process and a backend process. The frontend process may include use of resources to write the data to a temporary data store (e.g., a non-volatile data store, NVRAM, a cache, etc.) and reducing the data (e.g., via pattern recognition, data deduplication, and compressing the data). The frontend process may be performed inline such that the data storage system may notify, with low latency, a host from which the request was received that the data write has been performed. The backend process may include use of resources to write the data to the data store, which may include batch writing the reduced data to all-flash memory. 
     In operation  806 , the data storage system manages the data in the data store using one or more resources associated with the block containers. Operation  806  may include the data storage system performing one or more processes to use one or more resources of block container system  402  to manage the data in the data store. The processes may use any of the illustrative resources described herein. In certain examples, the managing of the data in the data store may include using resources of block container system  402  to reduce the data in the data store (e.g., by deep deduplication, deep compression, etc.) and/or reclaim storage (e.g., by garbage collection). 
       FIG. 9  illustrates an exemplary method  900  for reading data. While  FIG. 9  illustrates exemplary operations according to one embodiment, other embodiments may omit, add to, reorder, and/or modify any of the operations shown in  FIG. 9 . One or more of the operations shown in  FIG. 9  may be performed by data storage system  400 , any components included therein, and/or any implementation thereof. 
     In operation  902 , a data storage system receives a request to read data. The data storage system may receive the request from any suitable source, such as data storage service  602 , another data storage system configured to interface with the data storage system, or a host (e.g., a host controller, a host server, etc.). 
     The request may include any information indicating or associated with the data to be read. For example, the request may include information about the data to be read, such as a data identifier (a data file identifier, a data object identifier used by the data storage system to identify a data object in which the object data is stored, etc.). Data fields included in the request may by defined by a schema used to interface with the data storage system. 
     In operation  904 , the data storage system retrieves the data from the data store in response to the request received in operation  902 . The container storage system may use one or more block containers to retrieve the data from the data store. For example, the data storage system may use a block container that represents the data in the data store, to locate and retrieve the data. This may include using a data identifier to identify a handle of the block container and using the handle to access and use the block container to locate and retrieve the data. 
     Additionally, in certain examples, the data storage system may use a lookaside data structure, such as a lookaside table or cache, to locate and retrieve the data. The data storage system may be configured to generate and populate the lookaside data structure with data representing a log of actions that are to be performed by the data storage system in a time-shifted manner, such as later as part of a background process. For example, the lookaside data structure may indicate tuples that are written to a silo but not yet written to the data store. Accordingly, the data storage system may use the lookaside data structure, together with a block container, to locate and retrieve data being read. 
       FIG. 10  illustrates an exemplary method  1000  for using a block container to represent data in a data store. While  FIG. 10  illustrates exemplary operations according to one embodiment, other embodiments may omit, add to, reorder, and/or modify any of the operations shown in  FIG. 10 . One or more of the operations shown in  FIG. 10  may be performed by a data storage system, any components included therein, and/or any implementation thereof. 
     In operation  1002 , a data storage system receives a request to write data to a data store. The data storage system may receive the request from any suitable source, such as a data storage service  602 , another data storage system configured to interface with the data storage system, or a host (e.g., a host controller, a host server, etc.). 
     The request may include any information indicating or associated with the request. For example, the request may include data to be written and information about the data, such as a data identifier (a data file identifier, a data object identifier to be used by the data storage system to identify a data instance to which the data is written), size information for the data, etc. Data fields included in the request may by defined by a schema used to interface with the data storage system. 
     In operation  1004 , the data storage system uses a block container to store the data to the data store in response to the request received in operation  1002 . The data storage system uses a handle having a unique identifier mapped to the block container to identify the block container. 
     In certain examples, the storing of the data to the data store may include the data storage system performing multi-stage processes such as a frontend process and a backend process. The frontend process may include writing the data to a temporary data store (e.g., a non-volatile data store, NVRAM, a cache, etc.) and reducing the data (e.g., via pattern recognition, data deduplication, and compressing the data). The frontend process may be performed inline such that the data storage system may notify a host, with low latency, that the data write has been performed. The backend process may include writing the data to the data store, which may include batch writing the reduced data to all-flash memory. 
     In operation  1006 , the data storage system receives a request to read the data from the data store. The data storage system may receive the request from any suitable source, such as a data storage service  602 , another data storage system configured to interface with the container storage system, or a host (e.g., a host controller, a host server, etc.). 
     The request may include any information indicating or associated with the data to be read. For example, the request may include information about the data to be read, such as a data identifier (a data file identifier, a data object identifier used by the data storage system to identify a data object in which the data is stored, etc.). Data fields included in the request may by defined by a schema used to interface with the data storage system. 
     In operation  1008 , the data storage system retrieves the data from the data store using the handle. This may include using the handle to identify and use the block container to locate and retrieve the data. Operation  1008  may additionally include searching a lookaside data structure to identify any data indicating that the block container is to be used for retrieval of data (e.g., data from a snapshot of the data that is not yet carried through to an updated version of the data). 
       FIG. 11  illustrates an exemplary method  1100  for using a block container to represent data in a data store. While  FIG. 11  illustrates exemplary operations according to one embodiment, other embodiments may omit, add to, reorder, and/or modify any of the operations shown in  FIG. 11 . One or more of the operations shown in  FIG. 11  may be performed by a data storage system, any components included therein, and/or any implementation thereof. 
     In operation  1102 , a data storage system provides a block container (e.g., by thin provisioning the block container). Operation  1102 , which may be performed in response to a request to write data to a data store, may be performed in any suitable way. For example, the data storage system may provision the block container based on data to be written to the data store and/or further based on block containers that already exist in the data store. 
     In operation  1104 , the data storage system stores data representing a data instance to the block container. Operation  1104  may be performed in any suitable way, including by writing the data to logical and/or physical storage media locations and associating the block container to the logical storage media locations (e.g., logical addresses) mapped to the physical storage media locations. 
     In operation  1106 , the data storage system maps a unique identifier for the data instance to a handle of the block container. Operation  1106  may be performed in any suitable way, including by the data storage system receiving or generating the unique identifier for the data instance and associating the unique identifier with the block container (e.g., by storing the unique identifier in the block container). 
     In accordance with certain embodiments of the present disclosure, a snapshot management system may manage snapshots of data based on a data protection plan that includes defined snapshot schedules. For example, the snapshot management system may capture snapshots and set retention periods for the snapshots based on defined snapshot capture and retention schedules. The snapshot management system may eradicate snapshots based on expirations of the retention periods. In certain examples, the snapshot management system may be configured to perform such operations in a manner that enforces a defined data protection policy, minimizes resource usage associated with enforcement of the data protection policy, and/or assists a data storage system in maintaining immutability of policy-managed snapshots. 
     Additionally or alternatively, in certain examples, the snapshot management system may perform operations in a manner that weights resource usage (e.g., processing loads) associated with the management of snapshots toward the capture of snapshots and away from the eradication of snapshots. For example, the snapshot management system may be configured to determine when to capture snapshots and, in association with the capture of the snapshots, what retention periods to give the snapshots. Such determinations may be made based on a state of snapshots in a data storage system and on a set of rules defining a data protection policy. The snapshot management system may then eradicate snapshots based simply on expirations of the retention periods that were set for the snapshots. In certain examples, this may effectively reduce (e.g., “thin out”) the number of snapshots being maintained in a data storage system in a policy-based manner that maintains a defined data protection policy and/or without requiring resource intensive post-capture analysis of a state of snapshots to determine how to thin out snapshots. 
       FIG. 12  illustrates an example of a data storage system  1200  (“storage system  1200 ”) that includes a data store  1202  storing snapshots  1204  of data and a snapshot management system  1206  communicatively coupled to data store  1202 . Snapshot management system  1206  and data store  1202  may be communicatively coupled using any suitable data communication technologies. Components of storage system  1200  may include or be implemented by any suitable software, hardware, or combination of software and hardware configured to perform one or more of the operations of storage system  1200 . 
     Storage system  1200  may be configured to store and manage data, including in any of the ways described herein. Such data may be stored and managed in data store  1202  and/or any other data store. Storage system  1200  may include any suitable system for storing data, including any of the illustrative storage systems described herein. The stored data may be of any type of data, including structured data, unstructured data, file data, block data, object data, database data, another suitable type of data, or any combination or sub-combination thereof. 
     Data store  1202  may represent any suitable logical data storage media, physical storage media, and/or other data storage in which data is stored. Data store  1202  may include any of the illustrative data storages described herein. Data store  1202  may store data maintained by storage system  1200 . Additionally or alternatively, data store  1202  may store, in any suitable way, data representative of snapshots  1204  of data maintained by storage system  1200 . The data representative of snapshots may be searchable by snapshot management system  1206 . In certain examples, data representing snapshots may be maintained in a database implementation, such as a SQL implementation. Any other suitable searchable data store may be used in other examples. 
     Snapshots  1204  may represent the state of data at various points in time in storage system  1200 . Snapshots  1204  may represent such data in any suitable way and using any suitable data format, configuration, data references, copy mechanism (e.g., copy on write), etc. Snapshots  1204  may be used by storage system  1200  and/or a user of storage system  1200  to access copies of data as the data existed at points in time. Snapshots  1204  may include and/or be associated with (e.g., stored together with) information (e.g., metadata) about the snapshots  1204 , which information may include times of capture of the snapshots  1204 , retentions periods of the snapshots  1204 , expiration times or counter values of the snapshots  1204 , identifiers (e.g., names) for the snapshots  1204 , rules used to capture the snapshots  1204 , types of snapshots  1204  (e.g., policy-based snapshots, immutable snapshots, etc.). 
     Snapshot management system  1206  may be configured to manage snapshots, such as snapshots  1204  of storage system  1200 , by performing one or more of the snapshot management operations described herein. For example, snapshot management system  1206  may capture snapshots  1204  and set retention periods for snapshots  1204  based on defined snapshot capture and retention schedules. Snapshot management system  1206  may eradicate snapshots  1204  based on expirations of the retention periods. 
     In certain examples, snapshot management system  1206  may be configured to manage snapshots  1204  based on a set of snapshot rules  1208  (“rules  1208 ”) defining a data protection policy for storage system  1200 . Snapshot management system  1200  may be configured to apply the rules  1208  to manage snapshots of any data of storage system  1200 . For example, the set of rules  1208  may be applied to one or more target data structures of storage system  1200 . Such a data structure may include any data entity or organization of data, such as a directory of a file storage system (e.g., a root directory, a managed directory, or any other suitable directory), a volume of a block storage system, an object store of an object storage system, a pod, a database, or any other grouping of data. 
     The set of rules  1208  may define an overall snapshot schedule for snapshots of data. The overall snapshot schedule may include an overall snapshot capture schedule and an overall snapshot retention schedule. Each overall snapshot schedule may include multiple individual schedules that together make up the overall snapshot schedule. For example, the set of rules  1208  may include multiple rules each defining an individual snapshot capture schedule and a corresponding individual snapshot retention schedule. The snapshot capture schedules of the rules  1208  may define an overall snapshot capture schedule of a data protection policy, and the snapshot retention schedules of the rules  1208  may define an overall snapshot retention schedule of the data protection policy. In certain examples, each rule in a set of rules may include defined values (e.g., user-defined values) for the same rule attributes. Examples of such rule attributes are described herein. 
     Based on the set of rules  1208  and a state of snapshots  1204 , snapshot management system  1206  may determine when to take snapshots  1204  and which rules in the set of rules  1208  to use to capture the snapshots  1204 . In association with the capture of the snapshots  1204  (e.g., as part of a capture process) and based on the selected rules  1208  that are used to capture the snapshots  1204 , snapshot management system  1206  may determine and set retention periods for the snapshots  1204 . Snapshot management system  1206  may eradicate snapshots  1204  from storage system  1200  based on expirations of the retention periods of the snapshots  1204 . Examples of defined sets of rules and how a snapshot management system such as snapshot management system  1206  may use the sets of rules to capture and eradicate snapshots are described herein. 
     While  FIG. 12  shows snapshot management system  1206  included in storage system  1200 , this is illustrative. Snapshot management system  1206  may be configured in any way suitable to manage snapshots of one or more storage systems. For example, snapshot management system  1206  may be communicatively coupled to and configured to manage snapshots of one or more storage systems, including storage  1200  and/or any other suitable storage system, in any way suitable. 
       FIGS. 13A-13C  depict illustrative implementations of snapshot management system  1206 .  FIG. 13A  shows a configuration  1300  in which snapshot management system  1206  is communicatively coupled to and configured to manage snapshots of multiple storage systems  1302 - 1  through  1302 -K (collectively “storage systems  1302 ”). Storage systems  1302  may include any suitable data storage systems, including any of the storage systems described herein. 
     Snapshot management system  1206  may concurrently manage snapshots of storage systems  1302  in any suitable way. In some examples, snapshot management system  1206  may apply a same set of snapshot rules to storage systems  1302 . Additionally or alternatively, snapshot management system  1206  may maintain and apply different sets of snapshot rules to respective storage systems  1302  (e.g., by applying one set of rules to storage system  1302 - 1  and another set of rules to storage system  1302 -K). Snapshot management system  1206  may capture snapshots by directing one or more storage systems  1302  to capture the snapshots and may eradicate snapshots by directing one or more storage systems  1302  to eradicate the snapshots from the one or more storage systems  1302 . 
       FIG. 13B  depicts an illustrative configuration  1310  in which snapshot management system  1206  is implemented in middleware  1312  that is configured to communicate with storage systems  1302 . Such an implementation may allow snapshot management system  1206  to manage snapshots of any storage system(s)  1302  with which middleware  1312  interacts. In certain examples, snapshot management system  1206  may access and use data from any of storage systems  1302  or another suitable source to load and initialize operation of snapshot management system  1206  in middleware  1312 . During operation, snapshot management system  1206  may use data stored in middleware  1312  (e.g., in a cache of middleware  1312 ) and/or access and use data from storage systems  1302  (e.g., by querying storage systems  1302  for information about active snapshots maintained in storage systems  1302 ) to perform one or more of the snapshot management operations described herein. This may allow the operations to be performed based on a world view of a data storage system that includes storage systems  1302 . 
     Middleware  1312  may be configured to provide a user interface  1314  through which a user of a data storage system may interact with a data storage system. Middleware  1312  may be configured to receive user input via user interface  1314  and to provide information and/or other content to a user via user interface  1314 . User interface  1314  may include one or more user interfaces, including for example a graphical user interface, a command line interface, an audio interface, a programmatic interface, a REST interface, a programmatic REST interface, any other suitable user interface, or any combination or sub-combination of such interfaces. 
     Snapshot management system  1206  may be configured to receive, by way of user interface  1314 , user input defining one or more data protection policies. For example, Snapshot management system  1206  may receive values for attributes of rules of a data protection policy. Additionally or alternatively, snapshot management system  1206  may provide, by way of user interface, information related to management of snapshots and/or operation of storage systems  1302 . 
     As an example, snapshot management system  1206  may present, by way of user interface  1314 , information about defined data protection policies, snapshot rules, and attributes of the rules. For instance, user interface  1314  may present information about snapshot capture and retention schedules specified by snapshot rules. Any information about data protection policies, snapshot rules, snapshots, and illustrative attributes of snapshot rules described herein may be presented in user interface  1314 . 
     As another example, snapshot management system  1206  may provide performance metrics related to operation of storage systems  1302  and/or operation of snapshot management for presentation in user interface  1314  (e.g., for display in a graphical user interface). The performance metrics may indicate storage space usage, latency of reads and writes, and/or other information about operation of each of storage systems  1302 . Performance metrics for each of storage systems  1302  may be presented individually and/or may be synced and/or aggregated by middleware  1312  for aggregated and/or concurrent presentation. For instance, presented performance metrics may include a first set of metrics for storage system  1302 - 1  (e.g., file storage system metrics) and a second set of metrics for storage system  1302 -K (e.g., block storage system metrics) synchronized for concurrent presentation in user interface  1314 . 
     Middleware  1312  may be configured to perform one or more operations to facilitate interaction between components of a data storage system, such as user interface  1314  and storage systems  1302 . To this end, middleware  1312  may be configured to access information from user interface  1314  and storage systems  1312 , generate a current world view of the information (e.g., by synchronizing the information), and use the current world view of the information to provide information to user interface  1314  and storage systems  1312 . In certain examples, middleware  1312  may be configured to use a binary protocol stream to interface with one or more of storage systems  1302 . Operations of middleware  1312  may be performed to provide crash consistency of data of storage systems  1302  in any suitable way, including by performing one or more data synchronization and/or handshaking operations to generate a synchronized world view of data. 
     While  FIG. 13B  shows snapshot management system  1206  implemented in middleware  1312 , snapshot management system  1206  may be implemented by any suitable component or components of a data storage system. In addition, while  FIG. 13B  shows middleware  1312  providing user interface  1314 , in other examples user interface  1314  may be provided by any other suitable component or components of a data storage system. 
     Snapshot management system  1206  may be configured to manage snapshots of multiple different storage systems, such as storage systems that store different types of data. For example, storage systems  1302  may be configured to store different types of data (e.g., file data and block data). In certain examples, snapshot management system  1206  may be configured to manage snapshots in a unified storage system, such as any unified storage system described herein. For example, snapshot management system  1206  may be configured to manage snapshots in storage system  600  or  604 . 
       FIG. 13C  depicts a configuration  1320  in which snapshot management system  1206  is communicatively coupled to block container system  402 , container utilization system  404 , and data storage service  602 , which are described above. Snapshot management system  1206  may be configured to manage, in any of the ways described herein, snapshots in one or more of block container system  402 , container utilization system  404 , and data storage service  602  (e.g., through direct and/or indirect communication with one or more of block container system  402 , container utilization system  404 , and data storage service  602 ). 
     Illustrative operations of a snapshot management system such as snapshot management system  1206 , sets of snapshot rules, and enforcement of snapshot rules will now be described. 
       FIG. 14A  depicts an illustrative set  1400  of snapshot rules. As shown, the set  1400  of rules includes three individual rules  1402 - 1 ,  1402 - 2 , and  1402 - 3 , which may be collectively referred to as rules  1402 . It will be understood that a set of rules may include any suitable number of individual rules. Each rule  1402  may define rule attributes such as a rule identifier, a snapshot capture schedule, and a snapshot retention schedule. For example, rule  1402 - 1  (“R_1”) includes a rule identifier  1404  (“Rule ID”) of “Rule1,” a snapshot capture schedule  1406  indicating that a snapshot is to be captured every hour (“every”=“1hr”), and a snapshot retention schedule  1408  indicating that a snapshot captured based on rule  1402 - 1  is to be kept for two hours (“Keep_for”=“2hr”). The snapshot retention schedule  1408  corresponds to the snapshot capture schedule  1406  such that each snapshot that is captured based on the snapshot capture schedule  1406  of rule  1402 - 1  is given a retention period indicated by the snapshot retention schedule  1408  of rule  1402 - 1 , in association with the capture (e.g., as part of a capture process). Rules  1402 - 2  and  1402 - 3  each similarly include a rule identifier, a snapshot capture schedule, and a corresponding snapshot retention schedule. 
     A rule identifier may include any information that may be used to identify a rule. Each rule in a set of rules may include a unique rule identifier in some examples. In other examples, at least some rules in a set of rules may share a common rule identifier. A snapshot management system may be configured to use rule identifiers to label captured snapshots as part of a capture process and to use the labels subsequently to identify snapshots and attributes of the snapshots. For example, the snapshot management system may use a snapshot label to identify a rule that was used to capture the snapshot and, from the rule, to identify one or more attributes (e.g., a retention period) of the snapshot. 
     The snapshot management system may use attributes of the snapshot in a snapshot capture process and/or a snapshot eradication process. For example, as part of a snapshot capture process, the snapshot management system may determine when to take a snapshot and which rule to use to take the snapshot based on a state of active snapshots, which state may be defined at least in part by the attributes of the snapshots (e.g., capture times and retention periods of the snapshots). As part of an eradication process, the snapshot management system may determine, based on attributes of the snapshots, when retention periods of the snapshots have expired. 
     A snapshot capture schedule of a rule may define any capture schedule to be followed by the snapshot management system to systematically capture snapshots. In the set  1400  of rules, each snapshot capture schedule defines a different frequency at which to capture snapshots, by defining different periods (e.g., lengths of time) between captures of snapshots (e.g., capture snapshots every hour, two hours, and four hours). The combination of individual snapshot capture schedules of rules  1402  forms an overall snapshot capture schedule for the set  1400  of rules. 
     A snapshot retention schedule of a rule may define any retention schedule to be followed by the snapshot management system in retaining and/or eradicating snapshots. In the set  1400  of rules, each snapshot retention schedule defines a different retention period for which to retain snapshots (e.g., keep snapshots for two hours, four hours, and eight hours). The combination of individual snapshot retention schedules of rules  1402  forms an overall snapshot retention schedule for the set  1400  of rules. 
     The snapshot management system may be configured to determine, based on the set  1400  of rules, when to take snapshots and which rules  1402  in the set  1400  of rules to use to take the snapshots. The snapshot management system may base the determination on the set  1400  of rules and a state of snapshots that have been captured based on rules  1402  in the set  1400  of rules. For example, the snapshot management system may analyze a current state of snapshots based on attributes of the snapshots in view of rules  1402  and, based on the analysis, determine when to take a snapshot and which rule in rules  1402  to use to take the snapshot to enforce the data protection policy defined by the set  1400  of rules. 
     A state of snapshots may include any information about snapshots at a point in time, including information indicating currently active snapshots that have been captured based on the set  1400  of rules and not yet eradicated. An analysis of the state of snapshots may include an analysis of one or more attributes of the snapshots, which may include comparing one or more attributes of the snapshots to attributes of rules in the set  1400  of rules. 
     Based on a result of a determination of when to capture a snapshot and/or which rule to use to capture the snapshot, the snapshot management system may capture a snapshot of data based on the selected rule. The capture may include the snapshot management system setting a retention period of the snapshot based on the snapshot retention schedule of the selected rule. For example, when the snapshot management system captures a snapshot based on rule  1402 - 1 , the snapshot management system sets, as part of the capture process, a retention period for the snapshot to be two hours based on the snapshot retention schedule  1408  of rule  1402 - 1 . Similarly, when the snapshot management system captures a snapshot based on rule  1402 - 2  or  1402 - 3 , the snapshot management system sets, as part of the capture process, a retention period for the snapshot to be four or eight hours based on the corresponding snapshot retention schedule of rule  1402 - 2  or  1402 - 3 . 
     The snapshot management system may be configured to perform a capture process to capture, based on a set of rules such as the set  1400  of rules shown in  FIG. 14A , a snapshot in any suitable way. To this end, the snapshot management system may perform one or more operations to capture the snapshot such that data representative of the snapshot is stored in a data store such as data store  1202 . For example, the snapshot management system may send one or more messages to direct a data storage system or one or more components of a data storage system (e.g., storage system  1302 , data storage service  602 , container utilization system  404 , block container system  402 , or any other suitable storage system) to capture a snapshot of data and store data representative of the snapshot in a data store. In certain examples, the capture process may include marking a captured snapshot or snapshot to be captured as a pending snapshot, batching the pending snapshot with any other pending snapshots, and sending the batch of pending snapshots to a storage system with a request that the data storage system create the snapshots in the storage system. In certain examples, the snapshot management system may be configured to check available resources (e.g., resources of a storage system) and modify a batch of pending snapshots and/or conditionally perform one or more operations of the capture process based on availability of resources. 
     A stored snapshot may include or be associated with any data representative of the snapshot and/or information about the snapshot. For example, the snapshot may include information indicating the rule used to capture the snapshot (e.g., by including a rule identifier in an identifier for the snapshot) and a capture time associated with capture of the snapshot. Additionally or alternatively, the snapshot may include information indicating the retention period of the snapshot. The retention period for the snapshot may be represented in any suitable way, such as with data indicating an expiration for the snapshot (e.g., an uptick count value, a time, or other value indicating when the snapshot expires), data indicating a capture time and retention period for the snapshot, etc. In certain examples, the snapshot may include an index value associated with the snapshot, which index value may help identify the snapshot and/or when the snapshot was captured relative to other snapshots. 
     A capture time for a snapshot may be defined in any suitable way and may be based on any operation in a capture process. For example, a capture time may be a time determined by the snapshot management system for capture of the snapshot, a time that the snapshot management system sent an instruction to capture the snapshot to a storage system, a time that the storage system creates the snapshot and/or confirms creation of the storage system, or any other suitable time. 
     The snapshot management system may be configured to detect that a retention period of a snapshot has expired and eradicate the snapshot from storage system  1200  based on the detection that the retention period of the snapshot has expired. The snapshot management system may perform such operations in any suitable way. For example, the snapshot management system may access information for active snapshots stored in a data store such as data store  1202  and identify, from the information, any active snapshots having retention periods that have expired. If an active snapshot is determined to have a retention period that has expired, the snapshot management system eradicates the snapshot from the data store and consequently from a storage system. Eradication of a snapshot from a storage system may mean that the snapshot is removed from the storage system such that the snapshot is no longer recoverable, which removal may free up and/or allow reclamation of storage resources for other uses. 
     The snapshot management system may perform operations associated with eradication of snapshots as part of an eradication process. The process may include any operations associated with eradication of snapshots, including accessing information about active snapshots, determining whether retention periods of any of the active snapshots have expired, and eradicating snapshots that have expired retention periods. The snapshot management system may eradicate snapshots in any suitable way, including by directing a data storage system or one or more components of a data storage system to eradicate snapshots from a data store. 
     In certain examples, the snapshot management system may detect that a retention period of a snapshot has expired by determining that an uptick counter has reached or exceeded an uptick value that corresponds to an expiration of the retention period. The snapshot management system may use alternative ways of detecting an expiration of a retention period in other examples. 
     In certain examples, the snapshot management system may access an uptick service and use counter values of the uptick service to determine when retention periods of snapshots have expired. The uptick service may provide an uptick counter in any suitable way, such as using a bit sequence counter that counts up in seconds elapsed since the uptick service started. The uptick service may be implemented by the snapshot management, a storage system, or any suitable component of a storage system. In certain examples, the uptick service may be used by a storage system for any additional or alternative data storage processes, such as any other eradication processes. 
     In certain examples, the snapshot management system may perform an eradication process by simply determining whether retention periods of snapshots have expired and without any further analysis of active snapshots or a set of rules (e.g., the set  1400  of rules) used to capture the snapshots. This may minimize an amount of resources (e.g., processing resources) used for eradication of snapshots. 
       FIG. 14B  depicts a timeline  1410  of snapshot management based on the set  1400  of rules shown in  FIG. 14A . Timeline  1410  includes a horizontal axis that represents a progression of time from left to right. Each column in timeline  1410  represent a one-hour period. The left border of each column represents the start of the hour represented by the column. The left edge of timeline  1410  represents the start of the first hour, or time equal to zero. The right edge of timeline  1410  represents the end of the twelfth hour. Timeline  1410  also includes a vertical axis that represents an order in which snapshots are captured, with a first snapshot captured being represented at the top of timeline  1410  and subsequently captured snapshots being represented further down timeline  1410 . 
     In timeline  1410 , snapshots are represented by rectangles each having a left edge positioned relative to a creation time of the respective snapshot and a right edge positioned relative to an end of a retention period of the respective snapshot. The end of the retention period may generally coincide with an eradication of the respective snapshot. Accordingly, placement of the rectangles relative to the horizontal axis of timeline  1410  indicates when the snapshots are created, when the snapshots are active (e.g., stored and available for access), and when the snapshots are eradicated. 
     The representations of the snapshots in timeline  1410  are labeled to indicate an order in which the snapshots are captured and a rule that is used to capture each snapshot. For example, the snapshot labeled “Rule3.1” indicates that the snapshot is captured based on Rule3 (i.e., rule  1402 - 3  in  FIG. 14A ) and that the snapshot is the first snapshot captured, the snapshot labeled “Rule 1.2” indicates that the snapshot is captured based on Rule1 (i.e., rule  1402 - 1  in  FIG. 14A ) and that the snapshot is the second snapshot captured, and so on. Representations of snapshots captured based on Rule1 have a solid black fill pattern, representations of snapshots captured based on Rule2 have a diagonal crosshatch fill pattern, and representations of snapshots captured based on Rule3 have a diagonal line fill pattern. 
     When a snapshot management system manages snapshots based on the set  1400  of rules in  FIG. 14A , the snapshot management system may capture, retain, and eradicate snapshots as represented in  FIG. 14B . Examples of how snapshots may be managed based on the set  1400  of rules and as represented in  FIG. 14B  will now be described. 
     Based on the set  1400  of rules, the snapshot management system may determine when to capture snapshots. To this end, for example, the snapshot management system may be configured to analyze a state of active snapshots within a certain lookback period and determine whether an active snapshot has been captured within the lookback period. In certain examples, the lookback period for this determination may be approximately equal to the highest frequency snapshot capture schedule in the rules (i.e., the capture schedule with the shortest period between captures). In the set  1400  of rules, the value for this lookback period is based on the capture schedule of Rule1 (“every”=“1hr”). If an active snapshot has been captured within the previous hour, the snapshot management system may determine that it is not yet time to capture a new snapshot. Otherwise, the snapshot management system may determine that it is time to capture a new snapshot. Other suitable ways of determining when to take snapshots based on the rules may be used in other examples. 
     A small offset may be applied to the lookback period and/or to any other lookback period used by the snapshot management system. For example, the value for the lookback period described in the previous paragraph may be set to one hour minus a small offset such as one millisecond, for example. The application of the small offset to the lookback period may help prevent time drift in capture times and/or eradication times. 
     Based on the attributes of the rules in the set  1400  of rules, the snapshot management system may capture a new snapshot every hour, such as is shown in timeline  1410 . This capture frequency is based on the snapshot capture schedule  1406  of rule  1402 - 1  defining a minimum allowable time between captures of snapshots to be one hour. 
     Based on the set  1400  of rules, the snapshot management system may determine which rule to use to capture a snapshot. The snapshot management system may make this determination in any suitable way, including by systematically considering the rules for use in capturing the snapshot. In certain examples, the snapshot management system may consider the rules in a specific order, which order may be designed to minimize the resources used to enforce the data protection policy defined by the set  1400  of rules. For example, the snapshot management system may be configured to consider the rules in descending order of capture periods (e.g., lengths of time between captures) and/or retention periods. For the set  1400  of rules, for example, the snapshot management system may consider Rule3 first, Rule2 second, and Rule1 third. 
     Examples of the snapshot management system systematically determining when to take snapshots and which rules to use to take the snapshots, as well as when to eradicate the snapshots, will now be described with reference to timeline  1410 . 
     At the start of the first hour (time is equal to zero), the snapshot management system determines that a snapshot is to be taken based on there not being an active snapshot captured within the hour preceding the start of the first hour. The snapshot management system next determines which rule in the set  1400  of rules to use to capture the snapshot. To make this determination, the snapshot management system may be configured to consider the rules  1402  in descending order of capture period and/or retention period, by starting at Rule3, moving to Rule2 if Rule 3 is not used, and to Rule1 if Rule2 is not used. The consideration of a rule may include consideration of one or more attributes of the rule, such as the capture schedule and/or retention schedule of the rule. 
     When considering Rule3, the snapshot management system may analyze the state of active snapshots at the start of the first hour and determine whether an active snapshot having at least an eight-hour retention period has been taken within the four hours preceding the start of the first hour. If not, the snapshot management system selects and uses Rule3 to capture a snapshot at the start of the first hour. Based on the snapshot retention schedule of Rule3, the snapshot management system sets the retention period of the snapshot to eight hours. The snapshot management system names the snapshot based on the name of Rule3 and using an index value indicating an order or capture of snapshots. As shown in  FIG. 14B , the snapshot is named Rule3.1 and is scheduled to be retained for eight hours, at which time the snapshot management system will eradicate the Rule3.1 snapshot at the end of the eighth hour. 
     Because Rule3 is used to capture a snapshot at the start of the first hour, the policy to capture a snapshot every hour is satisfied, and the snapshot management system will abstain from capturing another snapshot until the start of the second hour. At the start of the second hour, the snapshot management system may determine that it is time to take another snapshot (because an hour has passed since a snapshot was captured) and may determine which rule in the set  1400  of rules to use to take the snapshot. To make this determination, the snapshot management system may again systematically consider the rules  1402  individually in descending order of capture period and/or retention period. 
     When considering Rule3, the snapshot management system may analyze the state of active snapshots at the start of the second hour and determine whether an active snapshot having at least an eight-hour retention period has been taken within the four hours preceding the start of the second hour. In this example, the Rule3.1 snapshot satisfies this condition, and the snapshot management system does not use Rule3 to capture a snapshot at the start of the second hour. The snapshot management system next considers Rule2 and determines whether an active snapshot having at least a four-hour retention period has been taken within the two hours preceding the start of the second hour. In this example, the Rule3.1 snapshot satisfies this condition as well, and the snapshot management system does not use Rule2 to capture a snapshot at the start of the second hour. 
     In certain examples, the snapshot management system may then default to using the last rule, Rule1, to capture a new snapshot at the start of the second hour. Based on the snapshot retention schedule of Rule1, the snapshot management system sets the retention period of the snapshot to two hours. The snapshot management system names the snapshot base on Rule1. As shown in  FIG. 14B , the snapshot is named Rule1.2 and is scheduled to be retained for two hours, at which time the snapshot management system will eradicate the Rule1.2 snapshot at the end of the third hour. 
     In other examples, instead of defaulting to Rule1, the snapshot management system may consider Rule1 and determine whether an active snapshot having at least a two-hour retention period has been taken within one hour preceding the start of the second hour. In this example, no active snapshot satisfies this condition because of the small offset applied to the one-hour lookback period, and the snapshot management system selects and uses Rule1 to capture a snapshot, the Rule1.2 snapshot, at the start of the second hour. 
     If a snapshot capture process were performed between the start of the first hour and the start of the second hour, the snapshot management system may determine that it is not time to capture a new snapshot because during that time period, the Rule3.1 snapshot was captured less than one hour ago. When the start of the second hour arrives, and because a small offset is applied to a lookback period, the snapshot management system will determine that it is time to take a new snapshot. 
     The snapshot management system may be configured to perform a snapshot capture process at any suitable time and/or frequency. For example, the snapshot management system may be configured to perform a snapshot capture process every hour. As another example, the snapshot management system may be configured to perform a snapshot capture process more frequently, such as every thirty seconds, which may help monitor for and remedy violations of and/or gaps in the data protection policy. An example of this is described further below. Similarly, the snapshot management system may be configured to perform a snapshot eradication process at any suitable time and/or frequency, such as every hour or every thirty seconds, for example. 
     Continuing the description of the example illustrated in timeline  1410 , at the start of the third hour, the snapshot management system may determine that it is time to take another snapshot (because an hour has passed since a snapshot was captured) and may determine which rule in the set  1400  of rules to use to take the snapshot. To make this determination, the snapshot management system may again systematically consider the rules  1402  individually in descending order of capture period and/or retention period. 
     When considering Rule3, the snapshot management system may analyze the state of active snapshots at the start of the third hour and determine whether an active snapshot having at least an eight-hour retention period has been taken within the four hours preceding the start of the second hour. In this example, the Rule3.1 snapshot satisfies this condition, and the snapshot management system does not use Rule3 to capture a snapshot at the start of the third hour. The snapshot management system next considers Rule2 and determines whether an active snapshot having at least a four-hour retention period has been taken within the two hours preceding the start of the second hour. In this example, neither the Rule3.1 snapshot nor the Rule1.2 snapshot satisfies this condition (because of the small offset applied to the lookback period, the Rule3.1 snapshot was captured just prior to the lookback period), and the snapshot management system selects and uses Rule2 to capture a snapshot at the start of the third hour. Based on the snapshot retention schedule of Rule2, the snapshot management system sets the retention period of the snapshot to four hours. The snapshot management system names the snapshot base on Rule2. As shown in  FIG. 14B , the snapshot is named Rule2.3 and is scheduled to be retained for four hours, at which time the snapshot management system will eradicate the Rule2.3 snapshot at the end of the sixth hour. 
     At the start of the fourth hour, the snapshot management system determines that the retention period for the Rule1.2 snapshot has expired and eradicates the Rule1.2 snapshot. Accordingly, at the start of the fourth hour, the Rule1.2 snapshot is no longer considered an active snapshot. 
     Also at the start of the fourth hour, the snapshot management system may determine that it is time to take another snapshot (because an hour has passed since a snapshot was captured) and may determine which rule in the set  1400  of rules to use to take the snapshot. To make this determination, the snapshot management system may again systematically consider the rules  1402  individually in descending order of capture period and/or retention period. 
     When considering Rule3, the snapshot management system may analyze the state of active snapshots at the start of the fourth hour and determine whether an active snapshot having at least an eight-hour retention period has been taken within the four hours preceding the start of the second hour. In this example, the Rule3.1 snapshot satisfies this condition, and the snapshot management system next considers Rule2 and determines whether an active snapshot having at least a four-hour retention period has been taken within the two hours preceding the start of the second hour. In this example, the Rule2.3 snapshot satisfies this condition. The snapshot management system will then use Rule1 to capture a new snapshot at the start of the fourth hour, based on either defaulting to Rule1 or determining that the state of active snapshots does not satisfy a condition associated with the attributes of Rule1 as described above. Based on the snapshot retention schedule of Rule1, the snapshot management system sets the retention period of the snapshot to two hours. The snapshot management system names the snapshot base on Rule1. As shown in  FIG. 14B , the snapshot is named Rule1.4 and is scheduled to be retained for two hours, at which time the snapshot management system will eradicate the Rule1.4 snapshot at the end of the fifth hour. 
     The operations of the snapshot management system may continue in this manner such that a new snapshot is captured every hour based on a select rule and such that active snapshots are eradicated at the expirations of their retention periods. Timeline  1410  illustrates the capture of new snapshots up to the start of the eighth hour and the retention of those snapshots up to the end of the twelfth hour. 
     The snapshot management system captures and retains/eradicates snapshots based on the set  1400  of rules such that the data protection policy defined by the set  1400  of rules is enforced. In certain examples, this includes performing operations such that a certain state of active snapshots exists at the end of a cycle, which cycle may have a time period equal to the longest retention period defined by the set  1400  of rules. In the present example, that time period is eight hours based on Rule3 defining an eight-hour retention period. Thus, in timeline  1410 , the end of the eighth hour represents the end of one cycle. At the end of the cycle, the active snapshots include a Rule3.5 snapshot (allowing access to data at a point in time four hours ago), a Rule2.7 snapshot (allowing access to data at a point in time two hours ago), and a Rule 1.8 snapshot (allowing access to data at a point in time 1 hour ago), which have capture times that align with the capture periods specified by rules  1402 . The Rule3.1 snapshot may also be considered to be active at or just before the end of the eighth hour. Accordingly, at or just before the end of the eighth hour, access may be available to snapshots captured one hour, two hours, four hours, and eight hours before the end of the eighth hour. 
       FIG. 14C  depicts timeline  1410  with representations of select lookback periods that triggered capture of some of the snapshots. As shown, a lookback period  1412  of just under four hours back in time from the start of the fifth hour triggered a capture of the Rule3.5 snapshot based on Rule3, a lookback period  1414  of just under one hour back in time from the start of the sixth hour triggered a capture of the Rule1.6 snapshot based on Rule1, and a lookback period  1416  of just under two hours back in time from the start of the seventh hour triggered a capture of the Rule2.7 snapshot based on Rule2. 
     The above-described examples of lookback periods being used by the snapshot management system to trigger capture of snapshots are illustrative. In other examples, the snapshot management system may be configured to use lookback periods in any way suitable to determine when to take snapshots and which rules to use to take the snapshots. In yet other examples, the snapshot management system may be configured to use other suitable mechanisms in addition or alternative to lookback periods to determine when to take snapshots and which rules to use to take the snapshots. 
     As mentioned, the snapshot management system may be configured to monitor for and remedy violations of and/or gaps in a data protection policy defined by a set of rules.  FIG. 14C  shows an example timeline  1420  that illustrates the snapshot management system detecting and remedying a violation of the data protection policy defined by the set  1400  of rules. As shown in timeline  1420 , fifteen minutes after the start of the third hour, the Rule3.1 snapshot is deleted prior to the end of the retention period of the snapshot. The deletion of the snapshot is represented by reference number  1422 , and the expected remainder of the retention period of the snapshot is represented by arrow  1424 . The Rule3.1 snapshot may be unexpectedly deleted in any way. For example, a user of a data storage system may intentionally or unintentionally delete the snapshot. 
     In certain examples, the snapshot management system may be configured to detect the deletion of the snapshot and convert the snapshot from a policy-managed snapshot associated with the set  1400  of rules to another type of snapshot (e.g., a manual snapshot). After this conversion of the snapshot, the snapshot management system may no longer be considered an active snapshot associated with the set  1400  of rules. In certain examples, the snapshot management system or another component of a data storage system may be configured to manage the converted snapshot, which may include maintaining the converted snapshot for a predetermined time period (e.g., twenty-four hours) before eradicating the snapshot from the data storage system. This may provide a level of protection for recovering deleted snapshots during the predetermined time period. If the converted snapshot is recovered before its new eradication time, the snapshot management system may still no longer manage the recovered snapshot as an active snapshot related to the set  1400  of rules. Accordingly, timeline  1420  will be the same regardless of what happens to the converted snapshot after it is deleted. 
     Subsequent to the deletion of the Rule3.1 snapshot, the snapshot management system may perform a snapshot capture process to determine whether it is time to capture a new snapshot. For example, the snapshot management system may be configured to perform the snapshot capture process every thirty seconds. Accordingly, the snapshot management system may perform the snapshot capture process within thirty seconds of the Rule3.1 snapshot being deleted, or just after fifteen minutes past the start of the third hour in this example. 
     As part of the snapshot capture process, the snapshot management system may determine that there is no active snapshot that was captured within a lookback period of four hours and that has a retention period of at least eight hours. Based on this determination, the snapshot management system may determine that it is time to take a snapshot based on Rule3 and may capture the Rule3.4 snapshot just after fifteen minutes past the start of the third hour as shown in timeline  1420 . Subsequent captures of snapshots may then be based on the time that the Rule3.4 snapshot was captured. For example, the snapshot management system may capture the Rule1.5 snapshot approximately one hour after the capture time of the Rule3.4 snapshot as shown, and so on as illustrated in timeline  1420 . 
     While the above-described example is described in terms of an unexpected deletion of a snapshot triggering a capture of a new snapshot, any change to a snapshot associated with a set of rules may trigger the same or similar operations of the snapshot management system to remedy a policy violation caused by the change. 
     In certain examples, snapshots created based on the set  1400  of rules may be policy-based snapshots that are immutable from their creation until their eradication at expiration of their retention periods. In such examples, any change to such a snapshot before its schedule eradication may be unexpected and considered to violate the data protection policy defined by the set  1400  of rules. Accordingly, the snapshot management system may be configured to detect such a change to a snapshot, convert the snapshot from a policy-based snapshot associated with the set  1400  of rules to another type of snapshot (e.g., which type of snapshot may be retained/eradicated based on another policy), and perform one or more operations to remedy the violation, which operations may include capturing a new replacement snapshot as described herein. 
       FIGS. 14A-14D  relate to operations of a snapshot management system based on an example set  1400  of rules. A snapshot management system may be configured to operate based on additional or alternative suitable sets of rules. In certain examples, for instance, one or more rules in a set of rules may specify a specific time for the rule(s) to be applied. The snapshot management system may be configured to handle such a specific time by verifying and enforcing a data protection policy defined by the set of rules at the specified time. The snapshot management system may do this in any suitable way, including by treating the specific time as an override to other attributes (e.g., a snapshot capture schedule and/or a snapshot retention schedule) specified by the rule. For example, a rule may specify that a snapshot be captured daily at a specific time of day. Based on the rule, the snapshot management system may use the rule to capture a new snapshot only at the specific time of day each day. 
       FIG. 15A  depicts another illustrative set  1500  of snapshot rules. As shown, the set  1500  of rules includes two individual rules  1502 - 1  and  1502 - 2 , which may be collectively referred to as rules  1502 . Each rule  1502  may define a rule identifier, a snapshot capture schedule, and a snapshot retention schedule. For example, rule  1502 - 1  (“R_1”) includes a rule identifier  1504  (“Rule ID”) of “Daily,” a snapshot capture schedule  1506  indicating that a snapshot is to be captured every day (“every”=“1day”), and a snapshot retention schedule  1508  indicating that a snapshot captured based on rule  1502 - 1  is to be kept for two days (“Keep_for”=“2day”). Rule  1502 - 2  includes a rule identifier  1510  (“Rule ID”) of “Weekly,” a snapshot capture schedule  1512  indicating that a snapshot is to be captured every seven days (“every”=“7day”), and a snapshot retention schedule  1514  indicating that a snapshot captured based on rule  1502 - 2  is to be kept for one month (“Keep_for”=“1 month”). 
     In addition, rule  1501 - 1  indicates a specific time of day  1516  (“At”=“Noon”) at which rule  1502 - 1  is to be used to capture snapshots. Based on the specific time of day  1516 , the snapshot management system may capture snapshots based on rule  1502 - 1  only at the specific time (noon) each day. 
       FIG. 15B  depicts a timeline  1520  of snapshot management based on the set  1500  of rules shown in  FIG. 15A . The configuration and elements of timeline  1520  are similar to those of timeline  1410  but represent snapshot management based on a different set  1500  of rules. Based on the set  1500  of rules, the snapshot management system may manage snapshots as represented in timeline  1520  by determining when to capture snapshots and which rules to use to capture the snapshots and eradicating the snapshots in accordance with attributes of rules  1502 . The snapshot management system may perform operations like those described above to determine when to take snapshots and which rules to use to take the snapshots. In addition, the snapshot management system may use rule  1502 - 1  to capture snapshots only at noon each day as specified by rule  1502 - 1 . 
     In certain examples, a data protection policy defined by a set of rules may be changed, such as by being disabled or modified by a user of a snapshot management system. In response to a policy being disabled, the snapshot management system may cease to capture new snapshots based on the policy. the snapshot management system may continue to eradicate active snapshots associated with the policy based on the retention periods of the snapshots. In response to a policy being modified, the snapshot management system may begin to manage snapshots based on the modified policy. If the modification reduces a retention period to a shorter retention period, the snapshot management system may delete active snapshots that are now outdated due to the modification. The deleted snapshots may be recoverable for a defined period (e.g., twenty-four hours) before they are eradicated. 
     In certain examples, attributes of rules may have certain requirements. As an example, retention periods of rules may not be allowed to be smaller than the retention period of the rule having the shorted capture period. As another example, capture periods of rules may be required to be multiples of the shortest capture period in a rule set. As another example, a retention period of a rule may not be allowed to be shorter than a capture period of the rule. As another example, a retention period of a rule may be required to be a multiple of a capture period of the rule. 
       FIGS. 16-21  depict illustrative snapshot management methods. While  FIGS. 16-21  illustrate example operations according to certain embodiments, other embodiments may omit, add to, reorder, and/or modify any of the operations shown in  FIGS. 16-21 . Operations of any of the illustrative methods may be combined with operations of any of the other methods in any suitable way. One or more of the operations shown in  FIGS. 16-21  may be performed by a snapshot management system, any components included therein, and/or any implementation thereof. 
       FIG. 16  depicts an illustrative method  1600 . In operation  1602 , a snapshot management system maintains a set of rules defining a data protection plan. The snapshot management system may maintain the set of rules in any suitable way, such as by receiving, storing, and initializing the set of rules for use in managing snapshots of a data structure. Once the set of rules is initialized, the snapshot management system may manage snapshots based on the set of rules. Operation  1602  may be performed in any suitable way, including in any of the ways described herein. 
     In operation  1604 , the snapshot management system determines, based on a state of snapshots and the set of rules, when to take a snapshot and which rule to use to take the snapshot. Operation  1604  may be performed in any suitable way, including in any of the ways described herein. 
     In operation  1606 , the snapshot management system captures the snapshot and sets a retention period for the snapshot based on the selected rule. Operation  1606  may be performed in any suitable way, including in any of the ways described herein. 
     In operation  1608 , the snapshot management system eradicates the snapshot based on an expiration of the retention period of the snapshot. Operation  1608  may be performed in any suitable way, including in any of the ways described herein. 
       FIG. 17  depicts an illustrative method  1700 . In operation  1702 , a snapshot management system determines whether it is time to capture a snapshot. Operation  1702  may be performed in any suitable way, including in any of the ways described herein, such as by determining, based on attributes of a set of rules defining a data protection plan, whether it is time to capture a snapshot. If the snapshot management system determines that it is not time to capture a snapshot, method  1700  ends. If the snapshot management system determines that it is time to capture a snapshot, method  1700  continues as operation  1704 . 
     In operation  1704 , the snapshot management system determines whether to use a first rule to capture the snapshot. Operation  1704  may be performed in any suitable way, including in any of the ways described herein, such as by using a lookback period for the first rule to determine whether an active snapshot exists that satisfies capture and retention schedules specified by the first rule. If the snapshot management system determines that the first rule is to be used to capture the snapshot, method  1700  continues at operation  1710  where the snapshot management system captures the snapshot based on the selected first rule. Method  1700  then ends. On the other hand, if the snapshot management system determines that the first rule is not to be used to capture the snapshot, method  1700  continues from operation  1704  to operation  1706 . 
     In operation  1706 , the snapshot management system determines whether to use a second rule to capture the snapshot. Operation  1706  may be performed in any suitable way, including in any of the ways described herein, such as by using a lookback period for the second rule to determine whether an active snapshot exists that satisfies capture and retention schedules specified by the second rule. If the snapshot management system determines that the second rule is to be used to capture the snapshot, method  1700  continues at operation  1710  where the snapshot management system captures the snapshot based on the selected second rule. Method  1700  then ends. On the other hand, if the snapshot management system determines that the second rule is not to be used to capture the snapshot, method  1700  continues from operation  1706  to operation  1708 . 
     In operation  1708 , the snapshot management system selects a third rule to be used to capture the snapshot (e.g., the third rule is a default rule to be used when the first and second rules are not used). Method  1700  continues from operation  1708  to operation  1710  where the snapshot management system captures the snapshot based on the selected third rule. 
     Method  1700  illustrates operations that may be performed based on a set of rules that includes three rules (e.g., set  1400  of rules). Method  1700  may be modified to accommodate any suitable number of rules included in a set of rules defining a data protection plan. Method  1700  illustrates one way that the snapshot management system may determine when to capture a snapshot and which rule to use to capture the snapshot. The snapshot management system may be configured to make such determinations in any other suitable way in other examples, such as in the example shown in  FIG. 18 . 
       FIG. 18  depicts an illustrative method  1800 . In operation  1802 , a snapshot management system determines whether to use a first rule in a set of rules to capture a snapshot. Operation  1802  may be performed in any suitable way, including in any of the ways described herein, such as by using a lookback period for the first rule to determine whether an active snapshot exists that satisfies capture and retention schedules specified by the first rule. If the snapshot management system determines that the first rule is to be used to capture the snapshot, method  1800  continues at operation  1808  where the snapshot management system captures the snapshot based on the selected first rule. Method  1800  then ends. On the other hand, if the snapshot management system determines that the first rule is not to be used to capture the snapshot, method  1800  continues from operation  1802  to operation  1804 . 
     In operation  1804 , the snapshot management system determines whether there is another rule in the set of rules. If there is not another rule, method  1800  ends without the snapshot management system capturing a snapshot. If there is another rule, method  1800  continues from operation  1804  to operation  1806  where the snapshot management system determines whether to use a next rule in the set of rules to capture the snapshot. Operation  1806  may be performed in any suitable way, including in any of the ways described herein, such as by using a lookback period for the next rule to determine whether an active snapshot exists that satisfies capture and retention schedules specified by the next rule. If the snapshot management system determines that the next rule is to be used to capture the snapshot, method  1800  continues at operation  1808  where the snapshot management system captures the snapshot based on the selected next rule. Method  1800  then ends. On the other hand, if the snapshot management system determines that the next rule is not to be used to capture the snapshot, method  1800  continues from operation  1806  to operation  1804 . Operations  1804  and  1806  may be repeated until either a next rule is selected for use in capturing the snapshot in operation  1808  or the rules in the set of rules are exhausted without any of the rules being selected for use in capturing the snapshot. In the first situation, method  1800  effectively determines it is time to take a snapshot and selects a rule to use to take the snapshot. In the latter situation, method  1800  effectively determines that it is not time to capture a snapshot. 
       FIG. 19  depicts an illustrative method  1900 . In operation  1902 , a snapshot management system determines whether any retention periods of active snapshots have expired. Operation  1902  may be performed in any suitable way, including in any of the ways described herein. If no retention periods of active snapshots have expired, method  1900  ends without eradicating any active snapshots. If one or more expired retention periods are detected, method  1900  continues from operation  1902  to operation  1904 . 
     In operation  1904 , the snapshot management system eradicates any snapshots determined to have expired retention periods. Operation  1904  may be performed in any suitable way, including in any of the ways described herein. 
       FIG. 20  depicts an illustrative method  2000 . In operation  2002 , a snapshot management system captures a snapshot based on a snapshot creation schedule (e.g., a snapshot creation schedule specified by a snapshot rule in a set of rules the define a data protection plan). Operation  2002  may be performed in any suitable way, including in any of the ways described herein. 
     In operation  2004 , the snapshot management system sets a retention period of the snapshot based on a snapshot retention schedule (e.g., a snapshot retention schedule that corresponds to the snapshot capture schedule, such as a snapshot retention scheduled specified by the same snapshot rule that specifies the snapshot capture schedule). Operation  2004  may be performed in any suitable way, including in any of the ways described herein. 
     In operation  2006 , the snapshot management system detects a change to the snapshot (e.g., a deletion of the snapshot) before an expiration of the retention period of the snapshots. Operation  2006  may be performed in any suitable way, including in any of the ways described herein. 
     In operation  2008 , the snapshot management system captures an additional snapshot based on the change to the snapshot before the expiration of the retention period of the snapshot. The additional snapshot may be a replacement for the changed snapshot. Operation  2008  may be performed in any suitable way, including in any of the ways described herein. 
       FIG. 21  depicts an illustrative method  2100 . In operation  2102 , a snapshot management system provides a user interface for user definition of a data protection plan. Operation  2102  may be performed in any suitable way, including in any of the ways described herein to provide a user interface (e.g., user interface  1314 ) for use by a user to define a data protection plan for a data storage system. For example, the snapshot management system may provide a graphical user interface and one or more tools configured for use by a user of a data storage system to define a data protection plan (e.g., by defining a set of rules to be used by the snapshot management system to systematically capture and eradicate snapshot). 
     In operation  2104 , the snapshot management system receives user input defining the data protection plan. The user input may include any suitable form of user input, and the snapshot management system may receive the user input in any suitable way. 
     In operation  2106 , the snapshot management system provides a preview of management of snapshots based on the data protection plan. The preview may be provided in any suitable way and may include any content suitable to preview snapshot management based on the data protection plan. In certain examples, the preview may include a preview timeline, such as timeline  1410  or  1520 , displayed in a graphical user interface. 
     In operation  2108 , the snapshot management system initializes the data protection plan in a data storage system. The snapshot management system may initialize the data protection plan in a data storage system in any suitable way to initiate the snapshot management system managing snapshots based on the data protection plan. 
     In certain examples, the snapshot management system may perform one or more operations of any of the methods shown in  FIGS. 16-21  as part of a snapshot capture process that is periodically executed by the snapshot management system. For example, operations  1604  and  1606  of method  1600 , operations of method  1700 , operations of method  1800 , and/or operations of method  2000  may be performed as part of a snapshot capture process that is periodically executed. The snapshot management system may execute a snapshot capture process to determine whether enforcement of a data protection plan is up to date and to capture a snapshot if the data protection plan is not up to date, is about to become out of date, or is violated. 
     In certain examples, the snapshot management system may perform one or more operations of any of the methods shown in  FIGS. 16-21  as part of a snapshot eradication process that is periodically executed by the snapshot management system. For example, the snapshot management system may perform operation  1608  of method  1600  and/or operations of method  1900  as part of snapshot eradication process that is periodically executed. 
     In the preceding description, various exemplary embodiments have been described with reference to the accompanying drawings. It will, however, be evident that various modifications and changes may be made thereto, and additional embodiments may be implemented, without departing from the scope of the invention as set forth in the claims that follow. For example, certain features of one embodiment described herein may be combined with or substituted for features of another embodiment described herein. The description and drawings are accordingly to be regarded in an illustrative rather than a restrictive sense.