Patent Publication Number: US-2022232075-A1

Title: Distributed protocol endpoint services for data storage systems

Description:
BACKGROUND 
     Solid-state memory, such as flash, is currently in use in solid-state drives (SSD) to augment or replace conventional hard disk drives (HDD), writable CD (compact disk) or writable DVD (digital versatile disk) drives, collectively known as spinning media, and tape drives, for storage of large amounts of data. Flash and other solid-state memories have characteristics that differ from spinning media. Yet, many solid-state drives are designed to conform to hard disk drive standards for compatibility reasons, which makes it difficult to provide enhanced features or take advantage of unique aspects of flash and other solid-state memory. Spinning media are limited in the flexibility or variations of the connections communication paths between the storage units or storage nodes of conventional storage arrays. 
     It is within this context that the embodiments arise. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The described embodiments and the advantages thereof may best be understood by reference to the following description taken in conjunction with the accompanying drawings. These drawings in no way limit any changes in form and detail that may be made to the described embodiments by one skilled in the art without departing from the spirit and scope of the described embodiments. 
         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 figures 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 exemplary computing device  350  that may be specifically configured to perform one or more of the processes described herein. 
         FIG. 4  illustrates an example of a fleet of storage systems  376  for providing storage services (also referred to herein as ‘data services’). 
         FIG. 5  illustrates an example container system. 
         FIG. 6A  is a block diagram of a further embodiment of the storage cluster having one example of connectivity between and within storage nodes and storage units in accordance with some embodiments. 
         FIG. 6B  is a variation of the connectivity within the storage cluster of  FIG. 6A  in accordance with some embodiments. 
         FIG. 7  is a block diagram of a further embodiment of the storage cluster of  FIGS. 1-5 , suitable for data storage or a combination of data storage and computing in accordance with some embodiments. 
         FIG. 8A  is a block diagram of a further embodiment of connectivity within the storage cluster of  FIGS. 1-5 , with switches in accordance with some embodiments. 
         FIG. 8B  is a variation of connectivity within the storage cluster of  FIG. 8A , with the switches coupling the storage units in accordance with some embodiments. 
         FIG. 9A  is a block diagram of one example of an architecture for compute nodes coupled together for the storage cluster in accordance with some embodiments. 
         FIG. 9B  is a block diagram of a further embodiment of the storage cluster of  FIGS. 1-5 , with the compute nodes of  FIG. 9A  in accordance with some embodiments. 
         FIG. 9C  is a block diagram of a variation of the storage cluster with compute nodes of  FIG. 9B , depicting storage nodes, storage units and compute nodes in multiple chassis, all coupled together as one or more storage clusters and variations of connectivity within a chassis and between chassis in accordance with some embodiments. 
         FIG. 9D  is a block diagram of an embodiment of a storage cluster, depicting distributed protocol endpoints of the data storage systems in accordance with some embodiments. 
         FIG. 10A  is a flow diagram of a method for operating a storage cluster, which can be practiced on or by embodiments of the storage cluster, storage nodes and/or non-volatile solid state storages or storage units in accordance with some embodiments. 
         FIG. 10B  is a flow diagram of a method of using distributed protocol endpoints for a storage system in accordance with some embodiments. 
         FIG. 11  is an illustration showing an exemplary computing device which may implement the embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Example methods, apparatus, and products for a storage system 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 some 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 storage device 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 some 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 may 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 some 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 some 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 (‘PIE’) 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 drives  171 A-F. 
     In some 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 drives  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 some 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 instant, 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 some 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 be 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 some 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 some 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 some 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 some 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 some 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. 
     In some implementations, storage drive  171 A-F may be one or more zoned storage devices. In some implementations, the one or more zoned storage devices may be a shingled HDD. In some implementations, the one or more storage devices may be a flash-based SSD. In a zoned storage device, a zoned namespace on the zoned storage device can be addressed by groups of blocks that are grouped and aligned by a natural size, forming a number of addressable zones. In some implementations utilizing an SSD, the natural size may be based on the erase block size of the SSD. In some implementations, the zones of the zoned storage device may be defined during initialization of the zoned storage device. In some implementations, the zones may be defined dynamically as data is written to the zoned storage device. 
     In some implementations, zones may be heterogeneous, with some zones each being a page group and other zones being multiple page groups. In some implementations, some zones may correspond to an erase block and other zones may correspond to multiple erase blocks. In an implementation, zones may be any combination of differing numbers of pages in page groups and/or erase blocks, for heterogeneous mixes of programming modes, manufacturers, product types and/or product generations of storage devices, as applied to heterogeneous assemblies, upgrades, distributed storages, etc. In some implementations, zones may be defined as having usage characteristics, such as a property of supporting data with particular kinds of longevity (very short lived or very long lived, for example). These properties could be used by a zoned storage device to determine how the zone will be managed over the zone&#39;s expected lifetime. 
     It should be appreciated that a zone is a virtual construct. Any particular zone may not have a fixed location at a storage device. Until allocated, a zone may not have any location at a storage device. A zone may correspond to a number representing a chunk of virtually allocatable space that is the size of an erase block or other block size in various implementations. When the system allocates or opens a zone, zones get allocated to flash or other solid-state storage memory and, as the system writes to the zone, pages are written to that mapped flash or other solid-state storage memory of the zoned storage device. When the system closes the zone, the associated erase block(s) or other sized block(s) are completed. At some point in the future, the system may delete a zone which will free up the zone&#39;s allocated space. During its lifetime, a zone may be moved around to different locations of the zoned storage device, e.g., as the zoned storage device does internal maintenance. 
     In some implementations, the zones of the zoned storage device may be in different states. A zone may be in an empty state in which data has not been stored at the zone. An empty zone may be opened explicitly, or implicitly by writing data to the zone. This is the initial state for zones on a fresh zoned storage device, but may also be the result of a zone reset. In some implementations, an empty zone may have a designated location within the flash memory of the zoned storage device. In an implementation, the location of the empty zone may be chosen when the zone is first opened or first written to (or later if writes are buffered into memory). A zone may be in an open state either implicitly or explicitly, where a zone that is in an open state may be written to store data with write or append commands. In an implementation, a zone that is in an open state may also be written to using a copy command that copies data from a different zone. In some implementations, a zoned storage device may have a limit on the number of open zones at a particular time. 
     A zone in a closed state is a zone that has been partially written to, but has entered a closed state after issuing an explicit close operation. A zone in a closed state may be left available for future writes, but may reduce some of the run-time overhead consumed by keeping the zone in an open state. In some implementations, a zoned storage device may have a limit on the number of closed zones at a particular time. A zone in a full state is a zone that is storing data and can no longer be written to. A zone may be in a full state either after writes have written data to the entirety of the zone or as a result of a zone finish operation. Prior to a finish operation, a zone may or may not have been completely written. After a finish operation, however, the zone may not be opened a written to further without first performing a zone reset operation. 
     The mapping from a zone to an erase block (or to a shingled track in an HDD) may be arbitrary, dynamic, and hidden from view. The process of opening a zone may be an operation that allows a new zone to be dynamically mapped to underlying storage of the zoned storage device, and then allows data to be written through appending writes into the zone until the zone reaches capacity. The zone can be finished at any point, after which further data may not be written into the zone. When the data stored at the zone is no longer needed, the zone can be reset which effectively deletes the zone&#39;s content from the zoned storage device, making the physical storage held by that zone available for the subsequent storage of data. Once a zone has been written and finished, the zoned storage device ensures that the data stored at the zone is not lost until the zone is reset. In the time between writing the data to the zone and the resetting of the zone, the zone may be moved around between shingle tracks or erase blocks as part of maintenance operations within the zoned storage device, such as by copying data to keep the data refreshed or to handle memory cell aging in an SSD. 
     In some implementations utilizing an HDD, the resetting of the zone may allow the shingle tracks to be allocated to a new, opened zone that may be opened at some point in the future. In some implementations utilizing an SSD, the resetting of the zone may cause the associated physical erase block(s) of the zone to be erased and subsequently reused for the storage of data. In some implementations, the zoned storage device may have a limit on the number of open zones at a point in time to reduce the amount of overhead dedicated to keeping zones open. 
     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 device controller  119 . In one embodiment, storage device 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 stored 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 storage system  124  for data storage in accordance with some implementations. In one embodiment, storage 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. 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, storage 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 storage controller  125   a  to another storage 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 interne 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. In some examples, translation from the client protocol may occur at the client. For examples, the client may execute a service including an endpoint protocol to translate from the client protocol to the backend protocol of the storage cluster. In some examples, the endpoint protocol may be executed on a dedicated processing device of the client such as a data processing unit (DPU), intelligence processing unit (IPU), graphics processing unit (GPU), or the like. The dedicated processing device may intercept and translate data requests from the client to the storage cluster. 
     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  152  units 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 storage  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 storage  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 storage  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  FIG. 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 embodiments, authorities  168  operate to determine how operations will proceed against particular logical elements. Each of the logical elements may be operated on through a particular authority across a plurality of storage controllers of a storage system. The authorities  168  may communicate with the plurality of storage controllers so that the plurality of storage controllers collectively perform operations against those particular logical elements. 
     In embodiments, logical elements could be, for example, files, directories, object buckets, individual objects, delineated parts of files or objects, other forms of key-value pair databases, or tables. In embodiments, performing an operation can involve, for example, ensuring consistency, structural integrity, and/or recoverability with other operations against the same logical element, reading metadata and data associated with that logical element, determining what data should be written durably into the storage system to persist any changes for the operation, or where metadata and data can be determined to be stored across modular storage devices attached to a plurality of the storage controllers in the storage system. 
     In some embodiments the operations are token based transactions to efficiently communicate within a distributed system. Each transaction may be accompanied by or associated with a token, which gives permission to execute the transaction. The authorities  168  are able to maintain a pre-transaction state of the system until completion of the operation in some embodiments. The token based communication may be accomplished without a global lock across the system, and also enables restart of an operation in case of a disruption or other failure. 
     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. Modes 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  152  unit 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, pseudo-random 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 non-volatile solid state storage  152  units 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 non-volatile sold state storage  152  units 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  152  units of  FIGS. 2A-C . In this version, each non-volatile solid state storage  152  unit 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 non-volatile solid state storage  152  unit 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 non-volatile solid state storage  152  units 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 non-volatile solid state storage  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 non-volatile solid state storage  152  unit 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&#39; 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 front-end 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 . 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’), interne 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. 
     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. 
     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 system  306  and remote, cloud-based storage that is utilized by the storage system  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. 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. 
     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. 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 . 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. 
     Although the example depicted in  FIG. 3A  illustrates the storage system  306  being coupled for data communications with the cloud services provider  302 , in other embodiments the storage system  306  may be part of a hybrid cloud deployment in which private cloud elements (e.g., private cloud services, on-premises infrastructure, and so on) and public cloud elements (e.g., public cloud services, infrastructure, and so on that may be provided by one or more cloud services providers) are combined to form a single solution, with orchestration among the various platforms. Such a hybrid cloud deployment may leverage hybrid cloud management software such as, for example, Azure™ Arc from Microsoft™, that centralize the management of the hybrid cloud deployment to any infrastructure and enable the deployment of services anywhere. In such an example, the hybrid cloud management software may be configured to create, update, and delete resources (both physical and virtual) that form the hybrid cloud deployment, to allocate compute and storage to specific workloads, to monitor workloads and resources for performance, policy compliance, updates and patches, security status, or to perform a variety of other tasks. 
     Readers will appreciate that by pairing the storage systems described herein with one or more cloud services providers, various offerings may be enabled. For example, disaster recovery as a service (‘DRaaS’) may be provided where cloud resources are utilized to protect applications and data from disruption caused by disaster, including in embodiments where the storage systems may serve as the primary data store. In such embodiments, a total system backup may be taken that allows for business continuity in the event of system failure. In such embodiments, cloud data backup techniques (by themselves or as part of a larger DRaaS solution) may also be integrated into an overall solution that includes the storage systems and cloud services providers described herein. 
     The storage systems described herein, as well as the cloud services providers, may be utilized to provide a wide array of security features. For example, the storage systems may encrypt data at rest (and data may be sent to and from the storage systems encrypted) and may make use of Key Management-as-a-Service (‘KMaaS’) to manage encryption keys, keys for locking and unlocking storage devices, and so on. Likewise, cloud data security gateways or similar mechanisms may be utilized to ensure that data stored within the storage systems does not improperly end up being stored in the cloud as part of a cloud data backup operation. Furthermore, microsegmentation or identity-based-segmentation may be utilized in a data center that includes the storage systems or within the cloud services provider, to create secure zones in data centers and cloud deployments that enables the isolation of workloads from one another. 
     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. 3B  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 storage resources  308  depicted in  FIG. 3B  may also include racetrack memory (also referred to as domain-wall memory). Such racetrack memory may be embodied as a form of non-volatile, solid-state memory that relies on the intrinsic strength and orientation of the magnetic field created by an electron as it spins in addition to its electronic charge, in solid-state devices. Through the use of spin-coherent electric current to move magnetic domains along a nanoscopic permalloy wire, the domains may pass by magnetic read/write heads positioned near the wire as current is passed through the wire, which alter the domains to record patterns of bits. In order to create a racetrack memory device, many such wires and read/write elements may be packaged together. 
     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 example storage system  306  depicted in  FIG. 3B  may leverage the storage resources described above in a variety of different ways. For example, some portion of the storage resources may be utilized to serve as a write cache, storage resources within the storage system may be utilized as a read cache, or tiering may be achieved within the storage systems by placing data within the storage system in accordance with one or more tiering policies. 
     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 , interne 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. 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 data archiving, data backup, data replication, data snapshotting, data and database cloning, 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 system  306 . For example, the software resources  314  may include software modules that perform 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. 
     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 . For example, each of the cloud computing instances  320 ,  322  may execute on an Azure VM, where each Azure VM may include high speed temporary storage that may be leveraged as a cache (e.g., as a read cache). In one embodiment, the cloud computing instances  320 ,  322  may be embodied as Amazon Elastic Compute Cloud (‘EC 2 ’) 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,  110 B in  FIG. 1A  described above such as writing data 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 , monitoring and reporting of storage device 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  such as distinct EC 2  instances. 
     Readers will appreciate that other embodiments that do not include a primary and secondary controller 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  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 EC 2  M 5  instances that include one or more SSDs, as EC 2  R 5  instances that include one or more SSDs, as EC 2   13  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  such as, for example, as Amazon Elastic Block Store (‘EBS’) volumes. 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 yet another embodiment, high performance block storage resources such as one or more Azure Ultra Disks may be utilized as the NVRAM. 
     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  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  resources, but the software daemon  328 ,  332 ,  336  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 ’). 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 . In other embodiments, rather than using both the cloud computing instances  340   a ,  340   b ,  340   n  with local storage  330 ,  334 ,  338  (also referred to herein as ‘virtual drives’) and the cloud-based object storage  348  to store data, a persistent storage layer may be implemented in other ways. For example, one or more Azure Ultra disks may be used to persistently store data (e.g., after the data has been written to the NVRAM layer). In an embodiment where one or more Azure Ultra disks may be used to persistently store data, the usage of a cloud-based object storage  348  may be eliminated such that data is only stored persistently in the Azure Ultra disks without also writing the data to an object storage layer. 
     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. The software daemon  328 ,  332 ,  336  may therefore 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.    
     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.    
     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 EC 2  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. 
     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 EC 2  instance) such that the cloud-based storage system  318  can be scaled-up or scaled-out as needed. For example, if 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 , a 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. 
     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 in this disclosure may be useful for supporting various types of software applications. In fact, the storage systems may be ‘application aware’ in the sense that the storage systems may obtain, maintain, or otherwise have access to information describing connected applications (e.g., applications that utilize the storage systems) to optimize the operation of the storage system based on intelligence about the applications and their utilization patterns. For example, the storage system may optimize data layouts, optimize caching behaviors, optimize ‘QoS’ levels, or perform some other optimization that is designed to improve the storage performance that is experienced by the application. 
     As an example of one type of application that may be supported by the storage systems describe herein, the storage system  306  may be useful in supporting artificial intelligence (‘AI’) applications, database applications, XOps projects (e.g., DevOps projects, DataOps projects, MLOps projects, ModelOps projects, PlatformOps 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. 
     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. 
     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 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, including the development of multi-layer 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 AI techniques have 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. 
     In order for the storage systems described above to serve as a data hub or as part of an AI deployment, in some embodiments the storage systems may be configured to provide DMA between storage devices that are included in the storage systems and one or more GPUs that are used in an AI or big data analytics pipeline. The one or more GPUs may be coupled to the storage system, for example, via NVMe-over-Fabrics (‘NVMe-oF’) such that bottlenecks such as the host CPU can be bypassed and the storage system (or one of the components contained therein) can directly access GPU memory. In such an example, the storage systems may leverage API hooks to the GPUs to transfer data directly to the GPUs. For example, the GPUs may be embodied as Nvidia™ GPUs and the storage systems may support GPUDirect Storage (‘GDS’) software, or have similar proprietary software, that enables the storage system to transfer data to the GPUs via RDMA or similar mechanism. 
     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 and 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, including being leveraged as part of a composable data analytics pipeline where containerized analytics architectures, for example, make analytics capabilities more composable. 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. 
     The storage systems described above may also be configured to implement NVMe Zoned Namespaces. Through the use of NVMe Zoned Namespaces, the logical address space of a namespace is divided into zones. Each zone provides a logical block address range that must be written sequentially and explicitly reset before rewriting, thereby enabling the creation of namespaces that expose the natural boundaries of the device and offload management of internal mapping tables to the host. In order to implement NVMe Zoned Name Spaces (‘ZNS’), ZNS SSDs or some other form of zoned block devices may be utilized that expose a namespace logical address space using zones. With the zones aligned to the internal physical properties of the device, several inefficiencies in the placement of data can be eliminated. In such embodiments, each zone may be mapped, for example, to a separate application such that functions like wear levelling and garbage collection could be performed on a per-zone or per-application basis rather than across the entire device. In order to support ZNS, the storage controllers described herein may be configured with to interact with zoned block devices through the usage of, for example, the Linux™ kernel zoned block device interface or other tools. 
     The storage systems described above may also be configured to implement zoned storage in other ways such as, for example, through the usage of shingled magnetic recording (SMR) storage devices. In examples where zoned storage is used, device-managed embodiments may be deployed where the storage devices hide this complexity by managing it in the firmware, presenting an interface like any other storage device. Alternatively, zoned storage may be implemented via a host-managed embodiment that depends on the operating system to know how to handle the drive, and only write sequentially to certain regions of the drive. Zoned storage may similarly be implemented using a host-aware embodiment in which a combination of a drive managed and host managed implementation is deployed. 
     The storage systems described herein may be used to form a data lake. A data lake may operate as the first place that an organization&#39;s data flows to, where such data may be in a raw format. Metadata tagging may be implemented to facilitate searches of data elements in the data lake, especially in embodiments where the data lake contains multiple stores of data, in formats not easily accessible or readable (e.g., unstructured data, semi-structured data, structured data). From the data lake, data may go downstream to a data warehouse where data may be stored in a more processed, packaged, and consumable format. The storage systems described above may also be used to implement such a data warehouse. In addition, a data mart or data hub may allow for data that is even more easily consumed, where the storage systems described above may also be used to provide the underlying storage resources necessary for a data mart or data hub. In embodiments, queries the data lake may require a schema-on-read approach, where data is applied to a plan or schema as it is pulled out of a stored location, rather than as it goes into the stored location. 
     The storage systems described herein may also be configured implement a recovery point objective (‘RPO’), which may be establish by a user, established by an administrator, established as a system default, established as part of a storage class or service that the storage system is participating in the delivery of, or in some other way. A “recovery point objective” is a goal for the maximum time difference between the last update to a source dataset and the last recoverable replicated dataset update that would be correctly recoverable, given a reason to do so, from a continuously or frequently updated copy of the source dataset. An update is correctly recoverable if it properly takes into account all updates that were processed on the source dataset prior to the last recoverable replicated dataset update. 
     In synchronous replication, the RPO would be zero, meaning that under normal operation, all completed updates on the source dataset should be present and correctly recoverable on the copy dataset. In best effort nearly synchronous replication, the RPO can be as low as a few seconds. In snapshot-based replication, the RPO can be roughly calculated as the interval between snapshots plus the time to transfer the modifications between a previous already transferred snapshot and the most recent to-be-replicated snapshot. 
     If updates accumulate faster than they are replicated, then an RPO can be missed. If more data to be replicated accumulates between two snapshots, for snapshot-based replication, than can be replicated between taking the snapshot and replicating that snapshot&#39;s cumulative updates to the copy, then the RPO can be missed. If, again in snapshot-based replication, data to be replicated accumulates at a faster rate than could be transferred in the time between subsequent snapshots, then replication can start to fall further behind which can extend the miss between the expected recovery point objective and the actual recovery point that is represented by the last correctly replicated update. 
     The storage systems described above may also be part of a shared nothing storage cluster. In a shared nothing storage cluster, each node of the cluster has local storage and communicates with other nodes in the cluster through networks, where the storage used by the cluster is (in general) provided only by the storage connected to each individual node. A collection of nodes that are synchronously replicating a dataset may be one example of a shared nothing storage cluster, as each storage system has local storage and communicates to other storage systems through a network, where those storage systems do not (in general) use storage from somewhere else that they share access to through some kind of interconnect. In contrast, some of the storage systems described above are themselves built as a shared-storage cluster, since there are drive shelves that are shared by the paired controllers. Other storage systems described above, however, are built as a shared nothing storage cluster, as all storage is local to a particular node (e.g., a blade) and all communication is through networks that link the compute nodes together. 
     In other embodiments, other forms of a shared nothing storage cluster can include embodiments where any node in the cluster has a local copy of all storage they need, and where data is mirrored through a synchronous style of replication to other nodes in the cluster either to ensure that the data isn&#39;t lost or because other nodes are also using that storage. In such an embodiment, if a new cluster node needs some data, that data can be copied to the new node from other nodes that have copies of the data. 
     In some embodiments, mirror-copy-based shared storage clusters may store multiple copies of all the cluster&#39;s stored data, with each subset of data replicated to a particular set of nodes, and different subsets of data replicated to different sets of nodes. In some variations, embodiments may store all of the cluster&#39;s stored data in all nodes, whereas in other variations nodes may be divided up such that a first set of nodes will all store the same set of data and a second, different set of nodes will all store a different set of data. 
     Readers will appreciate that RAFT-based databases (e.g., etcd) may operate like shared-nothing storage clusters where all RAFT nodes store all data. The amount of data stored in a RAFT cluster, however, may be limited so that extra copies don&#39;t consume too much storage. A container server cluster might also be able to replicate all data to all cluster nodes, presuming the containers don&#39;t tend to be too large and their bulk data (the data manipulated by the applications that run in the containers) is stored elsewhere such as in an S3 cluster or an external file server. In such an example, the container storage may be provided by the cluster directly through its shared-nothing storage model, with those containers providing the images that form the execution environment for parts of an application or service. 
     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 . 
     For further explanation,  FIG. 4  illustrates an example of a fleet of storage systems  376  for providing storage services (also referred to herein as ‘data services’). The fleet of storage systems  376  depicted in  FIG. 3  includes a plurality of storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  that may each be similar to the storage systems described herein. The storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  in the fleet of storage systems  376  may be embodied as identical storage systems or as different types of storage systems. For example, two of the storage systems  374   a ,  374   n  depicted in  FIG. 4  are depicted as being cloud-based storage systems, as the resources that collectively form each of the storage systems  374   a ,  374   n  are provided by distinct cloud services providers  370 ,  372 . For example, the first cloud services provider  370  may be Amazon AWS™ whereas the second cloud services provider  372  is Microsoft Azure™, although in other embodiments one or more public clouds, private clouds, or combinations thereof may be used to provide the underlying resources that are used to form a particular storage system in the fleet of storage systems  376 . 
     The example depicted in  FIG. 4  includes an edge management service  366  for delivering storage services in accordance with some embodiments of the present disclosure. The storage services (also referred to herein as ‘data services’) that are delivered may include, for example, services to provide a certain amount of storage to a consumer, services to provide storage to a consumer in accordance with a predetermined service level agreement, services to provide storage to a consumer in accordance with predetermined regulatory requirements, and many others. 
     The edge management service  366  depicted in  FIG. 4  may be embodied, for example, as one or more modules of computer program instructions executing on computer hardware such as one or more computer processors. Alternatively, the edge management service  366  may be embodied as one or more modules of computer program instructions executing on a virtualized execution environment such as one or more virtual machines, in one or more containers, or in some other way. In other embodiments, the edge management service  366  may be embodied as a combination of the embodiments described above, including embodiments where the one or more modules of computer program instructions that are included in the edge management service  366  are distributed across multiple physical or virtual execution environments. 
     The edge management service  366  may operate as a gateway for providing storage services to storage consumers, where the storage services leverage storage offered by one or more storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n . For example, the edge management service  366  may be configured to provide storage services to host devices  378   a ,  378   b ,  378   c ,  378   d ,  378   n  that are executing one or more applications that consume the storage services. In such an example, the edge management service  366  may operate as a gateway between the host devices  378   a ,  378   b ,  378   c ,  378   d ,  378   n  and the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n , rather than requiring that the host devices  378   a ,  378   b ,  378   c ,  378   d ,  378   n  directly access the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n.    
     The edge management service  366  of  FIG. 4  exposes a storage services module  364  to the host devices  378   a ,  378   b ,  378   c ,  378   d ,  378   n  of  FIG. 4 , although in other embodiments the edge management service  366  may expose the storage services module  364  to other consumers of the various storage services. The various storage services may be presented to consumers via one or more user interfaces, via one or more APIs, or through some other mechanism provided by the storage services module  364 . As such, the storage services module  364  depicted in  FIG. 4  may be embodied as one or more modules of computer program instructions executing on physical hardware, on a virtualized execution environment, or combinations thereof, where executing such modules causes enables a consumer of storage services to be offered, select, and access the various storage services. 
     The edge management service  366  of  FIG. 4  also includes a system management services module  368 . The system management services module  368  of  FIG. 4  includes one or more modules of computer program instructions that, when executed, perform various operations in coordination with the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  to provide storage services to the host devices  378   a ,  378   b ,  378   c ,  378   d ,  378   n . The system management services module  368  may be configured, for example, to perform tasks such as provisioning storage resources from the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  via one or more APIs exposed by the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n , migrating datasets or workloads amongst the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  via one or more APIs exposed by the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n , setting one or more tunable parameters (i.e., one or more configurable settings) on the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  via one or more APIs exposed by the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n , and so on. For example, many of the services described below relate to embodiments where the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  are configured to operate in some way. In such examples, the system management services module  368  may be responsible for using APIs (or some other mechanism) provided by the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  to configure the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  to operate in the ways described below. 
     In addition to configuring the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n , the edge management service  366  itself may be configured to perform various tasks required to provide the various storage services. Consider an example in which the storage service includes a service that, when selected and applied, causes personally identifiable information (‘PII’) contained in a dataset to be obfuscated when the dataset is accessed. In such an example, the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  may be configured to obfuscate PII when servicing read requests directed to the dataset. Alternatively, the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  may service reads by returning data that includes the PII, but the edge management service  366  itself may obfuscate the PII as the data is passed through the edge management service  366  on its way from the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  to the host devices  378   a ,  378   b ,  378   c ,  378   d ,  378   n.    
     The storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  depicted in  FIG. 4  may be embodied as one or more of the storage systems described above with reference to  FIGS. 1A-3D , including variations thereof In fact, the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  may serve as a pool of storage resources where the individual components in that pool have different performance characteristics, different storage characteristics, and so on. For example, one of the storage systems  374   a  may be a cloud-based storage system, another storage system  374   b  may be a storage system that provides block storage, another storage system  374   c  may be a storage system that provides file storage, another storage system  374   d  may be a relatively high-performance storage system while another storage system  374   n  may be a relatively low-performance storage system, and so on. In alternative embodiments, only a single storage system may be present. 
     The storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  depicted in  FIG. 4  may also be organized into different failure domains so that the failure of one storage system  374   a  should be totally unrelated to the failure of another storage system  374   b . For example, each of the storage systems may receive power from independent power systems, each of the storage systems may be coupled for data communications over independent data communications networks, and so on. Furthermore, the storage systems in a first failure domain may be accessed via a first gateway whereas storage systems in a second failure domain may be accessed via a second gateway. For example, the first gateway may be a first instance of the edge management service  366  and the second gateway may be a second instance of the edge management service  366 , including embodiments where each instance is distinct, or each instance is part of a distributed edge management service  366 . 
     As an illustrative example of available storage services, storage services may be presented to a user that are associated with different levels of data protection. For example, storage services may be presented to the user that, when selected and enforced, guarantee the user that data associated with that user will be protected such that various recovery point objectives (‘RPO’) can be guaranteed. A first available storage service may ensure, for example, that some dataset associated with the user will be protected such that any data that is more than 5 seconds old can be recovered in the event of a failure of the primary data store whereas a second available storage service may ensure that the dataset that is associated with the user will be protected such that any data that is more than 5 minutes old can be recovered in the event of a failure of the primary data store. 
     An additional example of storage services that may be presented to a user, selected by a user, and ultimately applied to a dataset associated with the user can include one or more data compliance services. Such data compliance services may be embodied, for example, as services that may be provided to consumers (i.e., a user) the data compliance services to ensure that the user&#39;s datasets are managed in a way to adhere to various regulatory requirements. For example, one or more data compliance services may be offered to a user to ensure that the user&#39;s datasets are managed in a way so as to adhere to the General Data Protection Regulation (‘GDPR’), one or data compliance services may be offered to a user to ensure that the user&#39;s datasets are managed in a way so as to adhere to the Sarbanes-Oxley Act of 2002 (‘SOX’), or one or more data compliance services may be offered to a user to ensure that the user&#39;s datasets are managed in a way so as to adhere to some other regulatory act. In addition, the one or more data compliance services may be offered to a user to ensure that the user&#39;s datasets are managed in a way so as to adhere to some non-governmental guidance (e.g., to adhere to best practices for auditing purposes), the one or more data compliance services may be offered to a user to ensure that the user&#39;s datasets are managed in a way so as to adhere to a particular clients or organizations requirements, and so on. 
     In order to provide this particular data compliance service, the data compliance service may be presented to a user (e.g., via a GUI) and selected by the user. In response to receiving the selection of the particular data compliance service, one or more storage services policies may be applied to a dataset associated with the user to carry out the particular data compliance service. For example, a storage services policy may be applied requiring that the dataset be encrypted prior to be stored in a storage system, prior to being stored in a cloud environment, or prior to being stored elsewhere. In order to enforce this policy, a requirement may be enforced not only requiring that the dataset be encrypted when stored, but a requirement may be put in place requiring that the dataset be encrypted prior to transmitting the dataset (e.g., sending the dataset to another party). In such an example, a storage services policy may also be put in place requiring that any encryption keys used to encrypt the dataset are not stored on the same system that stores the dataset itself. Readers will appreciate that many other forms of data compliance services may be offered and implemented in accordance with embodiments of the present disclosure. 
     The storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  in the fleet of storage systems  376  may be managed collectively, for example, by one or more fleet management modules. The fleet management modules may be part of or separate from the system management services module  368  depicted in  FIG. 4 . The fleet management modules may perform tasks such as monitoring the health of each storage system in the fleet, initiating updates or upgrades on one or more storage systems in the fleet, migrating workloads for loading balancing or other performance purposes, and many other tasks. As such, and for many other reasons, the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n  may be coupled to each other via one or more data communications links in order to exchange data between the storage systems  374   a ,  374   b ,  374   c ,  374   d ,  374   n.    
     In some embodiments, one or more storage systems or one or more elements of storage systems (e.g., features, services, operations, components, etc. of storage systems), such as any of the illustrative storage systems or storage system elements described herein may be implemented in one or more container systems. A container system may include any system that supports execution of one or more containerized applications or services. Such a service may be software deployed as infrastructure for building applications, for operating a run-time environment, and/or as infrastructure for other services. In the discussion that follows, descriptions of containerized applications generally apply to containerized services as well. 
     A container may combine one or more elements of a containerized software application together with a runtime environment for operating those elements of the software application bundled into a single image. For example, each such container of a containerized application may include executable code of the software application and various dependencies, libraries, and/or other components, together with network configurations and configured access to additional resources, used by the elements of the software application within the particular container in order to enable operation of those elements. A containerized application can be represented as a collection of such containers that together represent all the elements of the application combined with the various run-time environments needed for all those elements to run. As a result, the containerized application may be abstracted away from host operating systems as a combined collection of lightweight and portable packages and configurations, where the containerized application may be uniformly deployed and consistently executed in different computing environments that use different container-compatible operating systems or different infrastructures. In some embodiments, a containerized application shares a kernel with a host computer system and executes as an isolated environment (an isolated collection of files and directories, processes, system and network resources, and configured access to additional resources and capabilities) that is isolated by an operating system of a host system in conjunction with a container management framework. When executed, a containerized application may provide one or more containerized workloads and/or services. 
     The container system may include and/or utilize a cluster of nodes. For example, the container system may be configured to manage deployment and execution of containerized applications on one or more nodes in a cluster. The containerized applications may utilize resources of the nodes, such as memory, processing and/or storage resources provided and/or accessed by the nodes. The storage resources may include any of the illustrative storage resources described herein and may include on-node resources such as a local tree of files and directories, off-node resources such as external networked file systems, databases or object stores, or both on-node and off-node resources. Access to additional resources and capabilities that could be configured for containers of a containerized application could include specialized computation capabilities such as GPUs and AI/ML engines, or specialized hardware such as sensors and cameras. 
     In some embodiments, the container system may include a container orchestration system (which may also be referred to as a container orchestrator, a container orchestration platform, etc.) designed to make it reasonably simple and for many use cases automated to deploy, scale, and manage containerized applications. In some embodiments, the container system may include a storage management system configured to provision and manage storage resources (e.g., virtual volumes) for private or shared use by cluster nodes and/or containers of containerized applications. 
       FIG. 5  illustrates an example container system  380 . In this example, the container system  380  includes a container storage system  381  that may be configured to perform one or more storage management operations to organize, provision, and manage storage resources for use by one or more containerized applications  382 - 1  through  382 -L of container system  380 . In particular, the container storage system  381  may organize storage resources into one or more storage pools  383  of storage resources for use by containerized applications  382 - 1  through  382 -L. The container storage system may itself be implemented as a containerized service. 
     The container system  380  may include or be implemented by one or more container orchestration systems, including Kubernetes™, Mesos™, Docker Swarm™, among others. The container orchestration system may manage the container system  380  running on a cluster  384  through services implemented by a control node, depicted as  385 , and may further manage the container storage system or the relationship between individual containers and their storage, memory and CPU limits, networking, and their access to additional resources or services. 
     A control plane of the container system  380  may implement services that include: deploying applications via a controller  386 , monitoring applications via the controller  386 , providing an interface via an API server  387 , and scheduling deployments via scheduler  388 . In this example, controller  386 , scheduler  388 , API server  387 , and container storage system  381  are implemented on a single node, node  385 . In other examples, for resiliency, the control plane may be implemented by multiple, redundant nodes, where if a node that is providing management services for the container system  380  fails, then another, redundant node may provide management services for the cluster  384 . 
     A data plane of the container system  380  may include a set of nodes that provides container runtimes for executing containerized applications. An individual node within the cluster  384  may execute a container runtime, such as Docker™, and execute a container manager, or node agent, such as a kubelet in Kubernetes (not depicted) that communicates with the control plane via a local network-connected agent (sometimes called a proxy), such as an agent  389 . The agent  389  may route network traffic to and from containers using, for example, Internet Protocol (IP) port numbers. For example, a containerized application may request a storage class from the control plane, where the request is handled by the container manager, and the container manager communicates the request to the control plane using the agent  389 . 
     Cluster  384  may include a set of nodes that run containers for managed containerized applications. A node may be a virtual or physical machine. A node may be a host system. 
     The container storage system  381  may orchestrate storage resources to provide storage to the container system  380 . For example, the container storage system  381  may provide persistent storage to containerized applications  382 - 1 - 382 -L using the storage pool  383 . The container storage system  381  may itself be deployed as a containerized application by a container orchestration system. 
     For example, the container storage system  381  application may be deployed within cluster  384  and perform management functions for providing storage to the containerized applications  382 . Management functions may include determining one or more storage pools from available storage resources, provisioning virtual volumes on one or more nodes, replicating data, responding to and recovering from host and network faults, or handling storage operations. The storage pool  383  may include storage resources from one or more local or remote sources, where the storage resources may be different types of storage, including, as examples, block storage, file storage, and object storage. 
     The container storage system  381  may also be deployed on a set of nodes for which persistent storage may be provided by the container orchestration system. In some examples, the container storage system  381  may be deployed on all nodes in a cluster  384  using, for example, a Kubernetes DaemonSet. In this example, nodes  390 - 1  through  390 -N provide a container runtime where container storage system  381  executes. In other examples, some, but not all nodes in a cluster may execute the container storage system  381 . 
     The container storage system  381  may handle storage on a node and communicate with the control plane of container system  380 , to provide dynamic volumes, including persistent volumes. A persistent volume may be mounted on a node as a virtual volume, such as virtual volumes  391 - 1  and  391 -P. After a virtual volume  391  is mounted, containerized applications may request and use, or be otherwise configured to use, storage provided by the virtual volume  391 . In this example, the container storage system  381  may install a driver on a kernel of a node, where the driver handles storage operations directed to the virtual volume. In this example, the driver may receive a storage operation directed to a virtual volume, and in response, the driver may perform the storage operation on one or more storage resources within the storage pool  383 , possibly under direction from or using additional logic within containers that implement the container storage system  381  as a containerized service. 
     The container storage system  381  may, in response to being deployed as a containerized service, determine available storage resources. For example, storage resources  392 - 1  through  392 -M may include local storage, remote storage (storage on a separate node in a cluster), or both local and remote storage. Storage resources may also include storage from external sources such as various combinations of block storage systems, file storage systems, and object storage systems. The storage resources  392 - 1  through  392 -M may include any type(s) and/or configuration(s) of storage resources (e.g., any of the illustrative storage resources described above), and the container storage system  381  may be configured to determine the available storage resources in any suitable way, including based on a configuration file. For example, a configuration file may specify account and authentication information for cloud-based object storage  348  or for a cloud-based storage system  318 . The container storage system  381  may also determine availability of one or more storage devices  356  or one or more storage systems. An aggregate amount of storage from one or more of storage device(s)  356 , storage system(s), cloud-based storage system(s)  318 , edge management services  366 , cloud-based object storage  348 , or any other storage resources, or any combination or sub-combination of such storage resources may be used to provide the storage pool  383 . The storage pool  383  is used to provision storage for the one or more virtual volumes mounted on one or more of the nodes  390  within cluster  384 . 
     In some implementations, the container storage system  381  may create multiple storage pools. For example, the container storage system  381  may aggregate storage resources of a same type into an individual storage pool. In this example, a storage type may be one of: a storage device  356 , a storage array  102 , a cloud-based storage system  318 , storage via an edge management service  366 , or a cloud-based object storage  348 . Or it could be storage configured with a certain level or type of redundancy or distribution, such as a particular combination of striping, mirroring, or erasure coding. 
     The container storage system  381  may execute within the cluster  384  as a containerized container storage system service, where instances of containers that implement elements of the containerized container storage system service may operate on different nodes within the cluster  384 . In this example, the containerized container storage system service may operate in conjunction with the container orchestration system of the container system  380  to handle storage operations, mount virtual volumes to provide storage to a node, aggregate available storage into a storage pool  383 , provision storage for a virtual volume from a storage pool  383 , generate backup data, replicate data between nodes, clusters, environments, among other storage system operations. In some examples, the containerized container storage system service may provide storage services across multiple clusters operating in distinct computing environments. For example, other storage system operations may include storage system operations described herein. Persistent storage provided by the containerized container storage system service may be used to implement stateful and/or resilient containerized applications. 
     The container storage system  381  may be configured to perform any suitable storage operations of a storage system. For example, the container storage system  381  may be configured to perform one or more of the illustrative storage management operations described herein to manage storage resources used by the container system. 
     In some embodiments, one or more storage operations, including one or more of the illustrative storage management operations described herein, may be containerized. For example, one or more storage operations may be implemented as one or more containerized applications configured to be executed to perform the storage operation(s). Such containerized storage operations may be executed in any suitable runtime environment to manage any storage system(s), including any of the illustrative storage systems described herein. 
     The storage systems described herein may support various forms of data replication. For example, two or more of the storage systems may synchronously replicate a dataset between each other. In synchronous replication, distinct copies of a particular dataset may be maintained by multiple storage systems, but all accesses (e.g., a read) of the dataset should yield consistent results regardless of which storage system the access was directed to. For example, a read directed to any of the storage systems that are synchronously replicating the dataset should return identical results. As such, while updates to the version of the dataset need not occur at exactly the same time, precautions must be taken to ensure consistent accesses to the dataset. For example, if an update (e.g., a write) that is directed to the dataset is received by a first storage system, the update may only be acknowledged as being completed if all storage systems that are synchronously replicating the dataset have applied the update to their copies of the dataset. In such an example, synchronous replication may be carried out through the use of I/O forwarding (e.g., a write received at a first storage system is forwarded to a second storage system), communications between the storage systems (e.g., each storage system indicating that it has completed the update), or in other ways. 
     In other embodiments, a dataset may be replicated through the use of checkpoints. In checkpoint-based replication (also referred to as ‘nearly synchronous replication’), a set of updates to a dataset (e.g., one or more write operations directed to the dataset) may occur between different checkpoints, such that a dataset has been updated to a specific checkpoint only if all updates to the dataset prior to the specific checkpoint have been completed. Consider an example in which a first storage system stores a live copy of a dataset that is being accessed by users of the dataset. In this example, assume that the dataset is being replicated from the first storage system to a second storage system using checkpoint-based replication. For example, the first storage system may send a first checkpoint (at time t=0) to the second storage system, followed by a first set of updates to the dataset, followed by a second checkpoint (at time t=1), followed by a second set of updates to the dataset, followed by a third checkpoint (at time t=2). In such an example, if the second storage system has performed all updates in the first set of updates but has not yet performed all updates in the second set of updates, the copy of the dataset that is stored on the second storage system may be up-to-date until the second checkpoint. Alternatively, if the second storage system has performed all updates in both the first set of updates and the second set of updates, the copy of the dataset that is stored on the second storage system may be up-to-date until the third checkpoint. Readers will appreciate that various types of checkpoints may be used (e.g., metadata only checkpoints), checkpoints may be spread out based on a variety of factors (e.g., time, number of operations, an RPO setting), and so on. 
     In other embodiments, a dataset may be replicated through snapshot-based replication (also referred to as ‘asynchronous replication’). In snapshot-based replication, snapshots of a dataset may be sent from a replication source such as a first storage system to a replication target such as a second storage system. In such an embodiment, each snapshot may include the entire dataset or a subset of the dataset such as, for example, only the portions of the dataset that have changed since the last snapshot was sent from the replication source to the replication target. Readers will appreciate that snapshots may be sent on-demand, based on a policy that takes a variety of factors into consideration (e.g., time, number of operations, an RPO setting), or in some other way. 
     The storage systems described above may, either alone or in combination, by configured to serve as a continuous data protection store. A continuous data protection store is a feature of a storage system that records updates to a dataset in such a way that consistent images of prior contents of the dataset can be accessed with a low time granularity (often on the order of seconds, or even less), and stretching back for a reasonable period of time (often hours or days). These allow access to very recent consistent points in time for the dataset, and also allow access to access to points in time for a dataset that might have just preceded some event that, for example, caused parts of the dataset to be corrupted or otherwise lost, while retaining close to the maximum number of updates that preceded that event. Conceptually, they are like a sequence of snapshots of a dataset taken very frequently and kept for a long period of time, though continuous data protection stores are often implemented quite differently from snapshots. A storage system implementing a data continuous data protection store may further provide a means of accessing these points in time, accessing one or more of these points in time as snapshots or as cloned copies, or reverting the dataset back to one of those recorded points in time. 
     Over time, to reduce overhead, some points in the time held in a continuous data protection store can be merged with other nearby points in time, essentially deleting some of these points in time from the store. This can reduce the capacity needed to store updates. It may also be possible to convert a limited number of these points in time into longer duration snapshots. For example, such a store might keep a low granularity sequence of points in time stretching back a few hours from the present, with some points in time merged or deleted to reduce overhead for up to an additional day. Stretching back in the past further than that, some of these points in time could be converted to snapshots representing consistent point-in-time images from only every few hours. 
     Although some embodiments are described largely in the context of a storage system, readers of skill in the art will recognize that embodiments of the present disclosure may also take the form of a computer program product disposed upon computer readable storage media for use with any suitable processing system. Such computer readable storage media may be any storage medium for machine-readable information, including magnetic media, optical media, solid-state media, or other suitable media. Examples of such media include magnetic disks in hard drives or diskettes, compact disks for optical drives, magnetic tape, and others as will occur to those of skill in the art. Persons skilled in the art will immediately recognize that any computer system having suitable programming means will be capable of executing the steps described herein as embodied in a computer program product. Persons skilled in the art will recognize also that, although some of the embodiments described in this specification are oriented to software installed and executing on computer hardware, nevertheless, alternative embodiments implemented as firmware or as hardware are well within the scope of the present disclosure. 
     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: 
     A method of receiving, by a processing device of a client device, a data request directed to a data storage system communicatively coupled to the client device; translating, by the processing device, the data request to a backend protocol of the data storage system; and retrieving, by the processing device, one or more portions of data from the data storage system based on the translated data request. 
     A system comprising a data storage system; and a client device communicatively coupled to the data storage system, the client device comprising a processing device to: receive a data request directed to the data storage system; translate the data request to a backend protocol of the data storage system; and retrieve one or more portions of data from the data storage system based on the translated data request. 
     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.  FIG. 6A  is a block diagram of a further embodiment of the storage cluster  160  of  FIGS. 1-5 . In this embodiment, the components are in a chassis  138 , such as the chassis  138  with multiple slots shown in  FIG. 1 . A power supply  606 , with a power distribution bus  172  (as seen in  FIG. 2 ), provides electrical power to the various components in the chassis  138 . Two storage nodes  150  are shown coupled to a pathway  604 , such as a network switch  620  in one embodiment. Further pathways are readily devised. The pathway  604  couples the storage nodes  150  to each other, and can also couple the storage nodes  150  to a network external to the chassis  138 , allowing connection to external devices, systems or networks. 
     Multiple storage units  152  are coupled to each other and to the storage nodes  150  by another pathway  602 , which is distinct from the network switch  620  or other pathway  604  coupling the storage nodes  150 . In one embodiment, the pathway  602  that couples the storage units  152  and the storage nodes  150  is a PCI Express bus (PCIe), although other busses, networks and various further couplings could be used. In some embodiments, there is transparent bridging for the storage node  150  to couple to the pathway  602 , e.g., to the PCI Express bus. 
     In order to connect to the two pathways  602 ,  604 , each storage node  150  has two ports  608 ,  610 . One of the ports  610  of each storage node  150  couples to one of the pathways  604 , the other port  608  of each storage node  150  couples to the other pathway  602 . 
     In some embodiments, each of the storage nodes  150  can perform compute functions as a compute node. For example, a storage node  150  could run one or more applications. Further, the storage nodes  150  can communicate with the storage units  152 , via the pathway  602 , to write and read user data (e.g., using erasure coding) as described with reference to  FIGS. 1-3  above. As another example, a storage node  150  executing one or more applications could make use of the user data, generating user data for storage in the storage units  152 , reading and processing the user data from the storage units  152 , etc. Even with loss of one of the storage units  152 , or in some embodiments, loss of two of the storage units  152 , the storage nodes  150  and/or remaining storage units  152  can still read the user data. 
     In some embodiments, the erasure coding functions are performed mostly or entirely in the storage units  152 , which frees up the computing power of the storage nodes  150 . This allows the storage nodes  150  to focus more on compute node duties, such as executing one or more applications. In some embodiments, the erasure coding functions are performed mostly or entirely in the storage nodes  150 . This allows the storage nodes  150  to focus more on storage node duties. In some embodiments, the erasure coding functions are shared across the storage nodes  150  and the storage units  152 . This allows the storage nodes  150  to have available computing bandwidth shared between compute node duties and storage node duties. 
     With the two pathways  602 ,  604  being distinct from each other, several advantages become apparent. Neither pathway  602 ,  604  becomes a bottleneck, as might happen if there were only one pathway coupling the storage nodes  150  and the storage units  152  to each other and to an external network. With only one pathway, a hostile could gain direct access to the storage units  152  without having to go through a storage node  150 . With two pathways  602 ,  604 , the storage nodes  150  can couple to each other through one pathway  604 , e.g., for multiprocessing applications or for inter-processor communication. The other pathway  602  can be used by either of the storage nodes  150  for data access in the storage units  152 . The architecture shown in  FIG. 6A  thus supports various storage and computing functions and scenarios. Particularly, one embodiment as shown in  FIG. 6A  is a storage and computing system in a single chassis  138 . Processing power, in the form of one or more storage nodes  150 , and storage capacity, in the form of one or more storage units  152 , can be added readily to the chassis  138  as storage and/or computing needs change. 
       FIG. 6B  is a variation of the storage cluster  160  of  FIG. 6A . In this version, the pathway  612  has portions specific to storage units  152  included in each storage node  150 . In one embodiment, the pathway  612  is implemented as a PCI Express bus coupling together storage units  152  and the storage node  150 . That is, the storage node  150  and storage units  152  in one blade share a PCI Express bus in some embodiments. The PCI Express bus is specific to the blade, and is not coupled directly to the PCI Express bus of another blade. Accordingly, storage units  152  in a blade can communicate with each other and with the storage node  150  in that blade. Communication from a storage unit  152  or a storage node  150  in one blade to a storage node  150  or storage unit  152  in another blade occurs via the network switch  620 , e.g., the pathway  614 . 
       FIG. 7  is a block diagram of a further embodiment of the storage cluster  160  of  FIGS. 1-5 , suitable for data storage or a combination of data storage and computing. The version of  FIG. 7  has all of the storage units  152  coupled together by a first pathway  616 , which could be a bus, a network or a hardwired mesh, among other possibilities. One storage node  150  is coupled to each of two storage units  152 . Another storage unit  152  is coupled to each of two further storage units  152 . The coupling from the storage nodes  150  to storage units  152  illustrates a second pathway  618 . 
       FIG. 8A  is a block diagram of a further embodiment of the storage cluster  160  of  FIGS. 1-5 , with switches  620 . One switch  620  couples all of the storage nodes  150  to each other. Another switch  620  also couples all of the storage nodes  150  together. In this embodiment, each storage node  150  has two ports, with each port connecting to one of the switches  620 . This arrangement of ports and switches  620  provides two paths for each storage node  150  to connect to any other storage node  150 . For example, the left-most storage node  150  can connect to the rightmost storage node  150  (or any other storage node  150  in the storage cluster  160 ) via a choice of either the first switch  620  or the second switch  620 . It should be appreciated that this architecture relieves communication bottlenecks. Further embodiments with one switch  620 , two switches  620  coupled to each other, or more than two switches  620 , and other numbers of ports, or networks, are readily devised in keeping with the teachings herein. 
       FIG. 8B  is a variation of the storage cluster  160  of  FIG. 8A , with the switches  620  coupling the storage units  152 . As in the embodiment in  FIG. 8A , the switches  620  couple the storage nodes  150 , providing two paths for each storage node  150  to communicate with any other storage node  150 . In addition, the switches  620  couple the storage units  152 . Two of the storage units  152  in each storage node  150  couple to one of the switches  620 , and one or more of the storage units  152  in each storage node  150  couple to another one of the switches  620 . In this manner, each storage unit  152  can connect to roughly half of the other storage units  152  in the storage cluster via one of the switches  620 . In a variation, the switches  620  are coupled to each other (as shown in the dashed line in  FIG. 8B ), and each storage unit can connect to any other storage unit  152  via the switches  620 . Further embodiments with one switch  620 , or other numbers of switches  620  and arrangements of connections and the number of components being connected are readily devised in keeping with the teachings herein. 
       FIG. 9A  is a block diagram of compute nodes  626  coupled together for the storage cluster  160 . A switch  620  couples all of the compute nodes  626  together, so that each compute node  626  can communicate with any other compute node  626  via the switch  620 . In various embodiments, each compute node  626  could be a compute-only storage node  150  or a specialized compute node  626 . In the embodiment shown, the compute node  626  has three processor complexes  628 . Each processor complex  628  has a port  630 , and may also have local memory and further support (e.g., digital signal processing, direct memory access, various forms of I/O, a graphics accelerator, one or more processors, and so on). Each port  630  is coupled to the switch  620 . Thus, each processor complex  628  can communicate with each other processor complex  628  via the associated port  630  and the switch  620 , in this architecture. In some embodiments, each processor complex  628  issues a heartbeat (a regular communication that can be observed as an indicator of ongoing operation, with the lack of a heartbeat signaling a possible failure or unavailability of the compute node or processor). In some embodiments, each compute node  626  issues a heartbeat. Storage nodes  150  and/or storage units  152  also issue heartbeats, in further embodiments. 
       FIG. 9B  is a block diagram of a further embodiment of the storage cluster  160  of  FIGS. 1-5 , with the compute nodes  626  of  FIG. 9A . This embodiment is also shown with storage nodes  150 . A switch  620  couples all ports of all of the storage nodes  150 , all ports of all of the compute nodes  626  (e.g., all processor complexes  628  of all of the compute nodes  626 ), and all storage units  152 . In variations, fewer or more storage nodes  150 , fewer or more compute nodes  626 , fewer or more storage units  152 , and fewer or more processor complexes  628  could be installed in the chassis  138 . Each storage node  150 , storage unit  152 , or compute node  626  could occupy one or more slots  142  (see  FIG. 1 ) in the chassis  138 . It should be appreciated that  FIGS. 9A and 9B  are one example and not meant to be limiting. In some embodiments multiple switches  620  may be integrated into chassis  138  and the compute nodes  626  may be coupled to the multiple switches in order to achieve the communication flexibility provided by the embodiments described herein, similar to the embodiments of  FIGS. 8A and 8B . 
       FIG. 9C  is a block diagram of a variation of the storage cluster  160  with compute nodes  626  of  FIG. 9B , depicting storage nodes  150 , storage units  152  and compute nodes  626  in multiple chassis  138 , all coupled together as one or more storage clusters  160 . Several chassis  138  could be rack-mounted and coupled together in the manner depicted, for expansion of a storage cluster  160 . In this embodiment, the switch  620  or switches  620  in each chassis  138  couple the components in the chassis  138  as described above with reference to  FIG. 9B , and the switch  620  or switches  620  in all of the chassis  138  are coupled together across all of the chassis  138 . With various combinations of storage nodes  150  and/or compute nodes  626 , storage capacity and/or compute capacity (e.g., for running applications, operating system(s), etc.) is readily configured and expanded or contracted, or virtualized in virtual computing environments. The use of switches  620  decreases or eliminates the usual patch wiring seen in many other rack-mounted systems. 
     Some embodiments of this and other versions of the storage cluster  160  can support two or more independent storage clusters, in one chassis  138 , two chassis  138 , or more chassis  138 . Each storage cluster  160  in a multi-storage cluster environment can have storage nodes  150 , storage units  152 , and/or compute nodes  626  in one, another, or both or more chassis  138 , in various combinations. For example, a first storage cluster  160  could have several storage nodes  150  in one chassis  138  and one or more storage nodes  150  in another chassis  138 . A second storage cluster  160  could have one or more storage nodes  150  in the first chassis  138  and one or more storage nodes  150  in the second chassis  138 . Either of these storage clusters  160  could have compute nodes  626  in either or both of the chassis  138 . Each storage cluster  160  could have its own operating system, and have its own applications executing, independently of the other storage cluster(s)  160 . 
       FIG. 9D  is a block diagram of an embodiment of a storage cluster  160 , depicting protocol endpoints distributed to client devices. A client device  902  includes a data processing unit (DPU)  905  executing protocol endpoints  906 . The protocol endpoints  906  may be protocols for receiving and translating a data access request of an application  904  of the client device  902  into backend protocols and requests directed to authorities of a data storage system  920 . The protocol endpoints  906  may intercept a data request from an application  904  executing on the client device  902 . The protocol endpoints  906  may then identify the authorities of the data storage system that are associated with the data requested by the application  904 . For example, the protocol endpoints  906  may apply a hash to the data requested by the application  904  to identify the authorities (e.g., authorities  168 ) of the storage system managing each portion of the requested data. In some examples, the application  904  may direct a data access request to the data storage system. The protocol endpoints  906  of the DPU  905  may intercept the request. The protocol endpoints  906  then identify the authorities  168  associated with the requested data. The protocol endpoints  906  then send separate requests to each of the identified authorities  168  associated with the requested data. The authorities  168  may then determine the actual storage locations of the data in storage units  914  and retrieve the data, as described herein. The authorities  168  may then return each respective portion of data to the protocol endpoints  906  through the Ethernet fabric  908 . The protocol endpoints  906  may then aggregate the returned portions of data received from the authorities  168  into a single coherent response to the data access request received from the application  904 . In some examples, the protocol endpoints  906  may similarly translate any data access request (e.g., read, writes, modify, etc.) into the backend protocols and provide the translated requests to authorities to perform the corresponding read, write, or modify operations at the data storage system, as described herein. 
     In some examples, the application  904  of the client device may be a database application, an operating system, or any other software that can communicate with the data storage system to store and retrieve data. The application  904  may request data using a front end protocol such as network file system (NFS) protocol, Simple Storage Service (S3), simple management protocol (SMP), or other language that receives and processes remote procedure calls (RPCs). The protocol endpoints  906  may be executed directly in the context of a network interface of the client device  902 . Accordingly, the application  904  continues to execute as though communicating directly with the data storage system but the data requests are actually being translated to the backend network language of the data storage system prior to transmitting the request from the client device  902 . In some examples, the DPU  905  and/or the protocol endpoint  906  on the client device  902  may store an authority matrix locally to allow the client device  902  to communicate directly with the authorities  168  on the storage blades  910  as well as locally reassemble the responses from the several authorities. The authority matrix may be a mapping of authorities to the blades and portions of a namespace the authority holds. The authority matrix may be updated at any time that the mapping of authorities to blades changes at the data storage system. 
     Although depicted as including a single client device  902 , any number of client devices communicating with the data storage system may execute a protocol endpoint. Additionally, although described herein as a DPU, any dedicated processing device may be used to execute the protocol endpoint  906  at the client device  902  (e.g., on a local DPU, IPU, or other accelerator device). Thus, upon scaling of the data storage system to include any number of storage blades in the storage array, the protocol endpoint can be distributed to client devices to prevent the protocol endpoint  906  from becoming a bottleneck in the data path of the storage blades  910  (e.g., due to limited throughput). For example, as client organizations scale, so does the number of client devices with DPUs. Additional DPUs executing the protocol endpoint  906  does not add additional points of contention, thus providing for unrestricted scalability. 
     Accordingly, by removing the protocol endpoint from the storage blades, load-balancing functionality in the data path is no longer needed. The fabric module, as described herein, for managing the distributed components of the data storage system may therefore become purely a software function that can be executed on a server or within any of the chassis of the cluster. The fabric module may thus perform the software defined networking and management components (e.g., authorities, etc.). 
     Although depicted and described within the context of a flash storage array, embodiments of the present disclosure may be used with any type of clustered file system. In some examples, the protocol endpoint may be designed to be compatible with one or more particular DPUs or other dedicated hardware. In other examples, the protocol endpoint may be designed to be generally compatible with any processing device. 
       FIG. 10A  is a flow diagram of a method for operating a storage cluster, which can be practiced on or by embodiments of the storage cluster, storage nodes and/or non-volatile solid state storages or storage units in accordance with some embodiments. In an action  1002 , a first storage unit receives a direction regarding metadata or a portion of user data, from a storage node of a storage cluster. For example, the direction could include a direction to store a portion of user data or a data shard, read a portion of user data or a data shard, construct data from data shards, read or write a parity shard, a direction to respond about health, status or performance, etc. 
     In an action  1004 , the first storage unit communicates directly with a second storage unit via a pathway that does not require assistance from any storage node or storage nodes. This communication could involve communicating about the metadata or the portion of user data. A suitable example of communication about the metadata is communication of a heartbeat (which relates to the direction to respond about health, status or performance). Examples of communication about the portion of the user data would be to request a data shard from another storage unit, or to send a parity shard to another storage unit for writing into flash memory of that storage unit. Further examples are readily devised in keeping with the teachings herein. In an action  1006 , the second storage unit receives the communication from the first storage unit, via the pathway. More specifically, the second storage unit receives the communication directly from the first storage unit, not from a storage node. 
     In an action  1008 , the second storage unit determines an action, based on the communication from the first storage unit. Depending on content of the communication, the second storage unit could store data, store metadata, read data or metadata and send it back to the first storage unit, respond to an inquiry from the first storage unit, and so on. A response, where appropriate, could be sent from the second storage unit back to the first storage unit, or to another storage unit, via a pathway that does not require assistance from any storage node or storage nodes. Or, the action could be for the second storage unit to communicate with one of the storage nodes, or a compute node. Further examples of actions are readily devised in keeping with the teachings herein. 
       FIG. 10B  is a flow diagram of a method of a distributed protocol endpoint for a storage system. The method can be practiced by embodiments of the DPU of a client device coupled to a storage cluster, and various embodiments of storage systems described herein. In an action  1010 , a processor of a client device receives a data access request directed to a data storage system. The data access request may be from an application or other software of the client device. The processor may be a DPU of a client device as depicted in  FIG. 9D . In action  1012 , the processor translates the data access request to a backend protocol of the data storage system. The processor may translate the data access request at the client device before being transmitted to the data storage system. In action  1014 , the processor transmits the translated data access request to authorities of the data storage system associated with data to be retrieved by the data access request. The processor may identify the authorities of the storage system that are associated with the data request (e.g., via an authority matrix) and transmit a request to each of the authorities (e.g., as translated to the backend language or protocol of the data storage system). In action  1016 , the processor receives responses from each of the authorities, the response including portions of the requested data. In action  1018 , the processor aggregates the portions of data received from the authorities. In action  1020 , the processor provides a response to the data access request to the client device, the response including the aggregated data. Accordingly, the processor provides a single coherent response to the requesting application of the client device. The protocol endpoint may also intercept and translate any other types of communications or data access requests (e.g., writes, modify, delete, etc.) between the client device and the data storage system. 
     It should be appreciated that the methods described herein may be performed with a digital processing system, such as a conventional, general-purpose computer system. Special purpose computers, which are designed or programmed to perform only one function may be used in the alternative.  FIG. 11  is an illustration showing an exemplary computing device which may implement the embodiments described herein. The computing device of  FIG. 11  may be used to perform embodiments of the functionality for a storage node or a non-volatile solid state storage unit in accordance with some embodiments. The computing device includes a central processing unit (CPU)  1101 , which is coupled through a bus  1105  to a memory  1103 , and mass storage device  1107 . Mass storage device  1107  represents a persistent data storage device such as a disc drive, which may be local or remote in some embodiments. The mass storage device  1107  could implement a backup storage, in some embodiments. Memory  1103  may include read only memory, random access memory, etc. Applications resident on the computing device may be stored on or accessed via a computer readable medium such as memory  1103  or mass storage device  1107  in some embodiments. Applications may also be in the form of modulated electronic signals modulated accessed via a network modem or other network interface of the computing device. It should be appreciated that CPU  1101  may be embodied in a general-purpose processor, a special purpose processor, or a specially programmed logic device in some embodiments. 
     Display  1111  is in communication with CPU  1101 , memory  1103 , and mass storage device  1107 , through bus  1105 . Display  1111  is configured to display any visualization tools or reports associated with the system described herein. Input/output device  1109  is coupled to bus  505  in order to communicate information in command selections to CPU  1101 . It should be appreciated that data to and from external devices may be communicated through the input/output device  1109 . CPU  1101  can be defined to execute the functionality described herein to enable the functionality described with reference to  FIGS. 1-6 . The code embodying this functionality may be stored within memory  1103  or mass storage device  1107  for execution by a processor such as CPU  1101  in some embodiments. The operating system on the computing device may be MS-WINDOWS™, UNIX™, LINUX™, iOS™, CentOS™, Android™, Redhat Linux™, z/OS™,or other known operating systems. It should be appreciated that the embodiments described herein may be integrated with virtualized computing system also. 
     Detailed illustrative embodiments are disclosed herein. However, specific functional details disclosed herein are merely representative for purposes of describing embodiments. Embodiments may, however, be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein. 
     It should be understood that although the terms first, second, etc. may be used herein to describe various steps or calculations, these steps or calculations should not be limited by these terms. These terms are only used to distinguish one step or calculation from another. For example, a first calculation could be termed a second calculation, and, similarly, a second step could be termed a first step, without departing from the scope of this disclosure. As used herein, the term “and/or” and the “/” symbol includes any and all combinations of one or more of the associated listed items. 
     As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “includes”, and/or “including”, when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Therefore, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. 
     It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may in fact be executed substantially concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved. 
     With the above embodiments in mind, it should be understood that the embodiments might employ various computer-implemented operations involving data stored in computer systems. These operations are those requiring physical manipulation of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. Further, the manipulations performed are often referred to in terms, such as producing, identifying, determining, or comparing. Any of the operations described herein that form part of the embodiments are useful machine operations. The embodiments also relate to a device or an apparatus for performing these operations. The apparatus can be specially constructed for the required purpose, or the apparatus can be a general-purpose computer selectively activated or configured by a computer program stored in the computer. In particular, various general-purpose machines can be used with computer programs written in accordance with the teachings herein, or it may be more convenient to construct a more specialized apparatus to perform the required operations. 
     A module, an application, a layer, an agent or other method-operable entity could be implemented as hardware, firmware, or a processor executing software, or combinations thereof. It should be appreciated that, where a software-based embodiment is disclosed herein, the software can be embodied in a physical machine such as a controller. For example, a controller could include a first module and a second module. A controller could be configured to perform various actions, e.g., of a method, an application, a layer or an agent. 
     The embodiments can also be embodied as computer readable code on a non-transitory computer readable medium. The computer readable medium is any data storage device that can store data, which can be thereafter read by a computer system. Examples of the computer readable medium include hard drives, network attached storage (NAS), read-only memory, random-access memory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes, and other optical and non-optical data storage devices. The computer readable medium can also be distributed over a network coupled computer system so that the computer readable code is stored and executed in a distributed fashion. Embodiments described herein may be practiced with various computer system configurations including hand-held devices, tablets, microprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers and the like. The embodiments can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a wire-based or wireless network. 
     Although the method operations were described in a specific order, it should be understood that other operations may be performed in between described operations, described operations may be adjusted so that they occur at slightly different times or the described operations may be distributed in a system which allows the occurrence of the processing operations at various intervals associated with the processing. 
     In various embodiments, one or more portions of the methods and mechanisms described herein may form part of a cloud-computing environment. In such embodiments, resources may be provided over the Internet as services according to one or more various models. Such models may include Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). In IaaS, computer infrastructure is delivered as a service. In such a case, the computing equipment is generally owned and operated by the service provider. In the PaaS model, software tools and underlying equipment used by developers to develop software solutions may be provided as a service and hosted by the service provider. SaaS typically includes a service provider licensing software as a service on demand. The service provider may host the software, or may deploy the software to a customer for a given period of time. Numerous combinations of the above models are possible and are contemplated. 
     Various units, circuits, or other components may be described or claimed as “configured to” perform a task or tasks. In such contexts, the phrase “configured to” is used to connote structure by indicating that the units/circuits/components include structure (e.g., circuitry) that performs the task or tasks during operation. As such, the unit/circuit/component can be said to be configured to perform the task even when the specified unit/circuit/component is not currently operational (e.g., is not on). The units/circuits/components used with the “configured to” language include hardware--for example, circuits, memory storing program instructions executable to implement the operation, etc. Reciting that a unit/circuit/component is “configured to” perform one or more tasks is expressly intended not to invoke 35 U.S.C. 112, sixth paragraph, for that unit/circuit/component. Additionally, “configured to” can include generic structure (e.g., generic circuitry) that is manipulated by software and/or firmware (e.g., an FPGA or a general-purpose processor executing software) to operate in manner that is capable of performing the task(s) at issue. “Configured to” may also include adapting a manufacturing process (e.g., a semiconductor fabrication facility) to fabricate devices (e.g., integrated circuits) that are adapted to implement or perform one or more tasks. 
     The foregoing description, for the purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the embodiments and its practical applications, to thereby enable others skilled in the art to best utilize the embodiments and various modifications as may be suited to the particular use contemplated. Accordingly, the present embodiments are to be considered as illustrative and not restrictive, and the invention is not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.