Patent Publication Number: US-2021191638-A1

Title: Voltage thresholds in flash devices

Description:
BACKGROUND 
     Flash memory is in increasing usage as storage memory in solid-state storage systems. Each flash memory cell can be erased, through removal of charge from a floating gate of the cell, and programmed, through addition of charge to the floating gate of the cell. To read a value from the cell, one bit for single level cell flash, and two or more bits for multilevel cell flash, a read voltage is applied to a control gate of the cell, and the cell either conducts or does not conduct, depending on whether the read voltage exceeds or does not exceed the threshold voltage for the cell. Threshold voltage (whether programmed or not) can drift over time, from charge loss (or gain), aging of circuitry, and other causes. Some flash memories have adjustable read voltage(s), to compensate for threshold voltage drift. One standard technique for storage systems is to apply a default read voltage until errors become unacceptable, then try another read voltage in attempt to recover data, and afterwards reverting to the default read voltage for subsequent accesses. With aging flash memory or long-term storage of data, this standard technique results in multiple retries and longer read times, which the user observes as longer latency for data reads. Therefore, there is a need in the art for a solution which overcomes the drawbacks described above. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         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. 4A  depicts effects of optimized and non-optimized read voltage levels on read values for an example single level cell flash memory. 
         FIG. 4B  depicts optimized read voltage levels and read values for an example multilevel cell flash memory with two bits per cell. 
         FIG. 4C  depicts optimized read voltage levels and read values for an example multilevel cell flash memory with three bits per cell, also known as triple level cell flash memory. 
         FIG. 5  depicts an example command for adjusting a read voltage level of a multilevel cell flash memory. 
         FIG. 6  sets forth a system diagram of a storage system that tracks bit flips and adjusts read voltage levels of multilevel cell flash memory. 
         FIG. 7  is a flow diagram of a method of biasing read voltage for flash memory in a storage system, which is practiced by the storage system and more specifically by one or more processors of the storage system. 
         FIG. 8  is an illustration showing an exemplary computing device which may implement the embodiments described herein. 
         FIG. 9  is a system action diagram depicting a storage system iterating adjustment of an optimal read voltage level for flash memory, in accordance with an embodiment of the description. 
         FIG. 10  is a flow diagram of a further method of biasing read voltage for flash memory in a storage system, which is practiced by the storage system and more specifically by one or more processors of the storage system. 
         FIG. 11  depicts a flash memory device architecture of word lines and bit lines in accordance with some embodiments. 
         FIG. 12  depicts a flash memory device architecture of pages within blocks, selected through a block decoder and page decoder, with data read-out through a page buffer lines in accordance with some embodiments. 
         FIG. 13  depicts a system that optimizes read voltage levels for accesses to pages or blocks to which a word line connects in accordance with some embodiments. 
         FIG. 14  is a flowchart diagram illustrating method operations for determining read voltage levels in a flash device in accordance with some embodiments. 
     
    
    
     Example methods, apparatus, and products for tracking and adjusting voltage thresholds for flash devices in accordance with embodiments of the present disclosure are described with reference to the accompanying drawings, beginning with  FIG. 1A .  FIG. 1A  illustrates an example system for data storage, in accordance with some implementations. System  100  (also referred to as “storage system” herein) includes numerous elements for purposes of illustration rather than limitation. It may be noted that system  100  may include the same, more, or fewer elements configured in the same or different manner in other implementations. 
     System  100  includes a number of computing devices  164 A-B. Computing devices (also referred to as “client devices” herein) may be embodied, for example, a server in a data center, a workstation, a personal computer, a notebook, or the like. Computing devices  164 A-B may be coupled for data communications to one or more storage arrays  102 A-B through a storage area network (‘SAN’)  158  or a local area network (‘LAN’)  160 . 
     The SAN  158  may be implemented with a variety of data communications fabrics, devices, and protocols. For example, the fabrics for SAN  158  may include Fibre Channel, Ethernet, Infiniband, Serial Attached Small Computer System Interface (‘SAS’), or the like. Data communications protocols for use with SAN  158  may include Advanced Technology Attachment (‘ATA’), Fibre Channel Protocol, Small Computer System Interface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’), HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or the like. It may be noted that SAN  158  is provided for illustration, rather than limitation. Other data communication couplings may be implemented between computing devices  164 A-B and storage arrays  102 A-B. 
     The LAN  160  may also be implemented with a variety of fabrics, devices, and protocols. For example, the fabrics for LAN  160  may include Ethernet ( 802 . 3 ), wireless ( 802 . 11 ), or the like. Data communication protocols for use in LAN  160  may include Transmission Control Protocol (‘TCP’), User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperText Transfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), Handheld Device Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’), Real Time Protocol (‘RTP’), or the like. 
     Storage arrays  102 A-B may provide persistent data storage for the computing devices  164 A-B. Storage array  102 A may be contained in a chassis (not shown), and storage array  102 B may be contained in another chassis (not shown), in implementations. Storage array  102 A and  102 B may include one or more storage array controllers  110 A-D (also referred to as “controller” herein). A storage array controller  110 A-D may be embodied as a module of automated computing machinery comprising computer hardware, computer software, or a combination of computer hardware and software. In some implementations, the storage array controllers  110 A-D may be configured to carry out various storage tasks. Storage tasks may include writing data received from the computing devices  164 A-B to storage array  102 A-B, erasing data from storage array  102 A-B, retrieving data from storage array  102 A-B and providing data to computing devices  164 A-B, monitoring and reporting of disk utilization and performance, performing redundancy operations, such as Redundant Array of Independent Drives (‘RAID’) or RAID-like data redundancy operations, compressing data, encrypting data, and so forth. 
     Storage array controller  110 A-D may be implemented in a variety of ways, including as a Field Programmable Gate Array (‘FPGA’), a Programmable Logic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’), System-on-Chip (‘SOC’), or any computing device that includes discrete components such as a processing device, central processing unit, computer memory, or various adapters. Storage array controller  110 A-D may include, for example, a data communications adapter configured to support communications via the SAN  158  or LAN  160 . In some implementations, storage array controller  110 A-D may be independently coupled to the LAN  160 . In implementations, storage array controller  110 A-D may include an I/O controller or the like that couples the storage array controller  110 A-D for data communications, through a midplane (not shown), to a persistent storage resource  170 A-B (also referred to as a “storage resource” herein). The persistent storage resource  170 A-B main include any number of storage drives  171 A-F (also referred to as “storage devices” herein) and any number of non-volatile Random Access Memory (‘NVRAM’) devices (not shown). 
     In some implementations, the NVRAM devices of a persistent storage resource  170 A-B may be configured to receive, from the storage array controller  110 A-D, data to be stored in the storage drives  171 A-F. In some examples, the data may originate from computing devices  164 A-B. In some examples, writing data to the NVRAM device may be carried out more quickly than directly writing data to the storage drive  171 A-F. In implementations, the storage array controller  110 A-D may be configured to utilize the NVRAM devices as a quickly accessible buffer for data destined to be written to the storage drives  171 A-F. Latency for write requests using NVRAM devices as a buffer may be improved relative to a system in which a storage array controller  110 A-D writes data directly to the storage drives  171 A-F. In some implementations, the NVRAM devices may be implemented with computer memory in the form of high bandwidth, low latency RAM. The NVRAM device is referred to as “non-volatile” because the NVRAM device may receive or include a unique power source that maintains the state of the RAM after main power loss to the NVRAM device. Such a power source may be a battery, one or more capacitors, or the like. In response to a power loss, the NVRAM device may be configured to write the contents of the RAM to a persistent storage, such as the storage drives  171 A-F. 
     In implementations, storage drive  171 A-F may refer to any device configured to record data persistently, where “persistently” or “persistent” refers as to a device&#39;s ability to maintain recorded data after loss of power. In some implementations, storage drive  171 A-F may correspond to non-disk storage media. For example, the storage drive  171 A-F may be one or more solid-state drives (‘SSDs’), flash memory based storage, any type of solid-state non-volatile memory, or any other type of non-mechanical storage device. In other implementations, storage drive  171 A-F may include mechanical or spinning hard disk, such as hard-disk drives (‘HDD’). 
     In some implementations, the storage array controllers  110 A-D may be configured for offloading device management responsibilities from storage drive  171 A-F in storage array  102 A-B. For example, storage array controllers  110 A-D may manage control information that may describe the state of one or more memory blocks in the storage drives  171 A-F. The control information may indicate, for example, that a particular memory block has failed and should no longer be written to, that a particular memory block contains boot code for a storage array controller  110 A-D, the number of program-erase (‘P/E’) cycles that have been performed on a particular memory block, the age of data stored in a particular memory block, the type of data that is stored in a particular memory block, and so forth. In some implementations, the control information may be stored with an associated memory block as metadata. In other implementations, the control information for the storage drives  171 A-F may be stored in one or more particular memory blocks of the storage drives  171 A-F that are selected by the storage array controller  110 A-D. The selected memory blocks may be tagged with an identifier indicating that the selected memory block contains control information. The identifier may be utilized by the storage array controllers  110 A-D in conjunction with storage drives  171 A-F to quickly identify the memory blocks that contain control information. For example, the storage controllers  110 A-D may issue a command to locate memory blocks that contain control information. It may be noted that control information may be so large that parts of the control information may be stored in multiple locations, that the control information may be stored in multiple locations for purposes of redundancy, for example, or that the control information may otherwise be distributed across multiple memory blocks in the storage drive  171 A-F. 
     In implementations, storage array controllers  110 A-D may offload device management responsibilities from storage drives  171 A-F of storage array  102 A-B by retrieving, from the storage drives  171 A-F, control information describing the state of one or more memory blocks in the storage drives  171 A-F. Retrieving the control information from the storage drives  171 A-F may be carried out, for example, by the storage array controller  110 A-D querying the storage drives  171 A-F for the location of control information for a particular storage drive  171 A-F. The storage drives  171 A-F may be configured to execute instructions that enable the storage drive  171 A-F to identify the location of the control information. The instructions may be executed by a controller (not shown) associated with or otherwise located on the storage drive  171 A-F and may cause the storage drive  171 A-F to scan a portion of each memory block to identify the memory blocks that store control information for the storage drives  171 A-F. The storage drives  171 A-F may respond by sending a response message to the storage array controller  110 A-D that includes the location of control information for the storage drive  171 A-F. Responsive to receiving the response message, storage array controllers  110 A-D may issue a request to read data stored at the address associated with the location of control information for the storage drives  171 A-F. 
     In other implementations, the storage array controllers  110 A-D may further offload device management responsibilities from storage drives  171 A-F by performing, in response to receiving the control information, a storage drive management operation. A storage drive management operation may include, for example, an operation that is typically performed by the storage drive  171 A-F (e.g., the controller (not shown) associated with a particular storage drive  171 A-F). A storage drive management operation may include, for example, ensuring that data is not written to failed memory blocks within the storage drive  171 A-F, ensuring that data is written to memory blocks within the storage drive  171 A-F in such a way that adequate wear leveling is achieved, and so forth. 
     In implementations, storage array  102 A-B may implement two or more storage array controllers  110 A-D. For example, storage array  102 A may include storage array controllers  110 A and storage array controllers  110 B. At a given instance, a single storage array controller  110 A-D (e.g., storage array controller  110 A) of a storage system  100  may be designated with primary status (also referred to as “primary controller” herein), and other storage array controllers  110 A-D (e.g., storage array controller  110 A) may be designated with secondary status (also referred to as “secondary controller” herein). The primary controller may have particular rights, such as permission to alter data in persistent storage resource  170 A-B (e.g., writing data to persistent storage resource  170 A-B). At least some of the rights of the primary controller may supersede the rights of the secondary controller. For instance, the secondary controller may not have permission to alter data in persistent storage resource  170 A-B when the primary controller has the right. The status of storage array controllers  110 A-D may change. For example, storage array controller  110 A may be designated with secondary status, and storage array controller  110 B may be designated with primary status. 
     In some implementations, a primary controller, such as storage array controller  110 A, may serve as the primary controller for one or more storage arrays  102 A-B, and a second controller, such as storage array controller  110 B, may serve as the secondary controller for the one or more storage arrays  102 A-B. For example, storage array controller  110 A may be the primary controller for storage array  102 A and storage array  102 B, and storage array controller  110 B may be the secondary controller for storage array  102 A and  102 B. In some implementations, storage array controllers  110 C and  110 D (also referred to as “storage processing modules”) may neither have primary or secondary status. Storage array controllers  110 C and  110 D, implemented as storage processing modules, may act as a communication interface between the primary and secondary controllers (e.g., storage array controllers  110 A and  110 B, respectively) and storage array  102 B. For example, storage array controller  110 A of storage array  102 A may send a write request, via SAN  158 , to storage array  102 B. The write request may be received by both storage array controllers  110 C and  110 D of storage array  102 B. Storage array controllers  110 C and  110 D facilitate the communication, e.g., send the write request to the appropriate storage drive  171 A-F. It may be noted that in some implementations storage processing modules may be used to increase the number of storage drives controlled by the primary and secondary controllers. 
     In implementations, storage array controllers  110 A-D are communicatively coupled, via a midplane (not shown), to one or more storage drives  171 A-F and to one or more NVRAM devices (not shown) that are included as part of a storage array  102 A-B. The storage array controllers  110 A-D may be coupled to the midplane via one or more data communication links and the midplane may be coupled to the storage drives  171 A-F and the NVRAM devices via one or more data communications links. The data communications links described herein are collectively illustrated by data communications links  108 A-D and may include a Peripheral Component Interconnect Express (‘PCIe’) bus, for example. 
       FIG. 1B  illustrates an example system for data storage, in accordance with some implementations. Storage array controller  101  illustrated in  FIG. 1B  may similar to the storage array controllers  110 A-D described with respect to  FIG. 1A . In one example, storage array controller  101  may be similar to storage array controller  110 A or storage array controller  110 B. Storage array controller  101  includes numerous elements for purposes of illustration rather than limitation. It may be noted that storage array controller  101  may include the same, more, or fewer elements configured in the same or different manner in other implementations. It may be noted that elements of  FIG. 1A  may be included below to help illustrate features of storage array controller  101 . 
     Storage array controller  101  may include one or more processing devices  104  and random access memory (‘RAM’)  111 . Processing device  104  (or controller  101 ) represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device  104  (or controller  101 ) may be a complex instruction set computing (‘CISC’) microprocessor, reduced instruction set computing (‘RISC’) microprocessor, very long instruction word (‘VLIW’) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device  104  (or controller  101 ) may also be one or more special-purpose processing devices such as an ASIC, an FPGA, a digital signal processor (‘DSP’), network processor, or the like. 
     The processing device  104  may be connected to the RAM  111  via a data communications link  106 , which may be embodied as a high speed memory bus such as a Double-Data Rate  4  (‘DDR4’) bus. Stored in RAM  111  is an operating system  112 . In some implementations, instructions  113  are stored in RAM  111 . Instructions  113  may include computer program instructions for performing operations in in a direct-mapped flash storage system. In one embodiment, a direct-mapped flash storage system is one that that addresses data blocks within flash drives directly and without an address translation performed by the storage controllers of the flash drives. 
     In implementations, storage array controller  101  includes one or more host bus adapters  103 A-C that are coupled to the processing device  104  via a data communications link  105 A-C. In implementations, host bus adapters  103 A-C may be computer hardware that connects a host system (e.g., the storage array controller) to other network and storage arrays. In some examples, host bus adapters  103 A-C may be a Fibre Channel adapter that enables the storage array controller  101  to connect to a SAN, an Ethernet adapter that enables the storage array controller  101  to connect to a LAN, or the like. Host bus adapters  103 A-C may be coupled to the processing device  104  via a data communications link  105 A-C such as, for example, a PCIe bus. 
     In implementations, storage array controller  101  may include a host bus adapter  114  that is coupled to an expander  115 . The expander  115  may be used to attach a host system to a larger number of storage drives. The expander  115  may, for example, be a SAS expander utilized to enable the host bus adapter  114  to attach to storage drives in an implementation where the host bus adapter  114  is embodied as a SAS controller. 
     In implementations, storage array controller  101  may include a switch  116  coupled to the processing device  104  via a data communications link  109 . The switch  116  may be a computer hardware device that can create multiple endpoints out of a single endpoint, thereby enabling multiple devices to share a single endpoint. The switch  116  may, for example, be a PCIe switch that is coupled to a PCIe bus (e.g., data communications link  109 ) and presents multiple PCIe connection points to the midplane. 
     In implementations, storage array controller  101  includes a data communications link  107  for coupling the storage array controller  101  to other storage array controllers. In some examples, data communications link  107  may be a QuickPath Interconnect (QPI) interconnect. 
     A traditional storage system that uses traditional flash drives may implement a process across the flash drives that are part of the traditional storage system. For example, a higher level process of the storage system may initiate and control a process across the flash drives. However, a flash drive of the traditional storage system may include its own storage controller that also performs the process. Thus, for the traditional storage system, a higher level process (e.g., initiated by the storage system) and a lower level process (e.g., initiated by a storage controller of the storage system) may both be performed. 
     To resolve various deficiencies of a traditional storage system, operations may be performed by higher level processes and not by the lower level processes. For example, the flash storage system may include flash drives that do not include storage controllers that provide the process. Thus, the operating system of the flash storage system itself may initiate and control the process. This may be accomplished by a direct-mapped flash storage system that addresses data blocks within the flash drives directly and without an address translation performed by the storage controllers of the flash drives. 
     In 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 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 implementations utilizing an SSD, the natural size may be based on the erase block size of the SSD. 
     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 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 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 controller  119 . In one embodiment, storage controller  119 A-D may be a CPU, ASIC, FPGA, or any other circuitry that may implement control structures necessary according to the present disclosure. In one embodiment, system  117  includes flash memory devices (e.g., including flash memory devices  120   a - n ), operatively coupled to various channels of the storage device controller  119 . Flash memory devices  120   a - n , may be presented to the controller  119 A-D as an addressable collection of Flash pages, erase blocks, and/or control elements sufficient to allow the storage device controller  119 A-D to program and retrieve various aspects of the Flash. In one embodiment, storage device controller  119 A-D may perform operations on flash memory devices  120   a - n  including storing and retrieving data content of pages, arranging and erasing any blocks, tracking statistics related to the use and reuse of Flash memory pages, erase blocks, and cells, tracking and predicting error codes and faults within the Flash memory, controlling voltage levels associated with programming and retrieving contents of Flash cells, etc. 
     In one embodiment, system  117  may include RAM  121  to store separately addressable fast-write data. In one embodiment, RAM  121  may be one or more separate discrete devices. In another embodiment, RAM  121  may be integrated into storage device controller  119 A-D or multiple storage device controllers. The RAM  121  may be utilized for other purposes as well, such as temporary program memory for a processing device (e.g., a CPU) in the storage device controller  119 . 
     In one embodiment, system  117  may include a stored energy device  122 , such as a rechargeable battery or a capacitor. Stored energy device  122  may store energy sufficient to power the storage device controller  119 , some amount of the RAM (e.g., RAM  121 ), and some amount of Flash memory (e.g., Flash memory  120   a - 120   n ) for sufficient time to write the contents of RAM to Flash memory. In one embodiment, storage device controller  119 A-D may write the contents of RAM to Flash Memory if the storage device controller detects loss of external power. 
     In one embodiment, system  117  includes two data communications links  123   a ,  123   b . In one embodiment, data communications links  123   a ,  123   b  may be PCI interfaces. In another embodiment, data communications links  123   a ,  123   b  may be based on other communications standards (e.g., HyperTransport, InfiniBand, etc.). Data communications links  123   a ,  123   b  may be based on non-volatile memory express (‘NVMe’) or NVMe over fabrics (‘NVMf’) specifications that allow external connection to the storage device controller  119 A-D from other components in the storage system  117 . It should be noted that data communications links may be interchangeably referred to herein as PCI buses for convenience. 
     System  117  may also include an external power source (not shown), which may be provided over one or both data communications links  123   a ,  123   b , or which may be provided separately. An alternative embodiment includes a separate Flash memory (not shown) dedicated for use in storing the content of RAM  121 . The storage device controller  119 A-D may present a logical device over a PCI bus which may include an addressable fast-write logical device, or a distinct part of the logical address space of the storage device  118 , which may be presented as PCI memory or as persistent storage. In one embodiment, operations to store into the device are directed into the RAM  121 . On power failure, the storage device controller  119 A-D may write stored content associated with the addressable fast-write logical storage to Flash memory (e.g., Flash memory  120   a - n ) for long-term persistent storage. 
     In one embodiment, the logical device may include some presentation of some or all of the content of the Flash memory devices  120   a - n , where that presentation allows a storage system including a storage device  118  (e.g., storage system  117 ) to directly address Flash memory pages and directly reprogram erase blocks from storage system components that are external to the storage device through the PCI bus. The presentation may also allow one or more of the external components to control and retrieve other aspects of the Flash memory including some or all of: tracking statistics related to use and reuse of Flash memory pages, erase blocks, and cells across all the Flash memory devices; tracking and predicting error codes and faults within and across the Flash memory devices; controlling voltage levels associated with programming and retrieving contents of Flash cells; etc. 
     In one embodiment, the stored energy device  122  may be sufficient to ensure completion of in-progress operations to the Flash memory devices  120   a - 120   n  stored energy device  122  may power storage device controller  119 A-D and associated Flash memory devices (e.g.,  120   a - n ) for those operations, as well as for the storing of fast-write RAM to Flash memory. Stored energy device  122  may be used to store accumulated statistics and other parameters kept and tracked by the Flash memory devices  120   a - n  and/or the storage device controller  119 . Separate capacitors or stored energy devices (such as smaller capacitors near or embedded within the Flash memory devices themselves) may be used for some or all of the operations described herein. 
     Various schemes may be used to track and optimize the life span of the stored energy component, such as adjusting voltage levels over time, partially discharging the storage energy device  122  to measure corresponding discharge characteristics, etc. If the available energy decreases over time, the effective available capacity of the addressable fast-write storage may be decreased to ensure that it can be written safely based on the currently available stored energy. 
       FIG. 1D  illustrates a third example system  124  for data storage in accordance with some implementations. In one embodiment, system  124  includes storage controllers  125   a ,  125   b . In one embodiment, storage controllers  125   a ,  125   b  are operatively coupled to Dual PCI storage devices  119   a ,  119   b  and  119   c ,  119   d , respectively. Storage controllers  125   a ,  125   b  may be operatively coupled (e.g., via a storage network  130 ) to some number of host computers  127   a - n.    
     In one embodiment, two storage controllers (e.g.,  125   a  and  125   b ) provide storage services, such as a SCS) block storage array, a file server, an object server, a database or data analytics service, etc. The storage controllers  125   a ,  125   b  may provide services through some number of network interfaces (e.g.,  126   a - d ) to host computers  127   a - n  outside of the storage system  124 . Storage controllers  125   a ,  125   b  may provide integrated services or an application entirely within the storage system  124 , forming a converged storage and compute system. The storage controllers  125   a ,  125   b  may utilize the fast write memory within or across storage devices  119   a - d  to journal in progress operations to ensure the operations are not lost on a power failure, storage controller removal, storage controller or storage system shutdown, or some fault of one or more software or hardware components within the storage system  124 . 
     In one embodiment, controllers  125   a ,  125   b  operate as PCI masters to one or the other PCI buses  128   a ,  128   b . In another embodiment,  128   a  and  128   b  may be based on other communications standards (e.g., HyperTransport, InfiniBand, etc.). Other storage system embodiments may operate storage controllers  125   a ,  125   b  as multi-masters for both PCI buses  128   a ,  128   b . Alternately, a PCI/NVMe/NVMf switching infrastructure or fabric may connect multiple storage controllers. Some storage system embodiments may allow storage devices to communicate with each other directly rather than communicating only with storage controllers. In one embodiment, a storage device controller  119   a  may be operable under direction from a storage controller  125   a  to synthesize and transfer data to be stored into Flash memory devices from data that has been stored in RAM (e.g., RAM  121  of  FIG. 1C ). For example, a recalculated version of RAM content may be transferred after a storage controller has determined that an operation has fully committed across the storage system, or when fast-write memory on the device has reached a certain used capacity, or after a certain amount of time, to ensure improve safety of the data or to release addressable fast-write capacity for reuse. This mechanism may be used, for example, to avoid a second transfer over a bus (e.g.,  128   a ,  128   b ) from the storage controllers  125   a ,  125   b . In one embodiment, a recalculation may include compressing data, attaching indexing or other metadata, combining multiple data segments together, performing erasure code calculations, etc. 
     In one embodiment, under direction from a storage controller  125   a ,  125   b , a storage device controller  119   a ,  119   b  may be operable to calculate and transfer data to other storage devices from data stored in RAM (e.g., RAM  121  of  FIG. 1C ) without involvement of the storage controllers  125   a ,  125   b . This operation may be used to mirror data stored in one controller  125   a  to another controller  125   b , or it could be used to offload compression, data aggregation, and/or erasure coding calculations and transfers to storage devices to reduce load on storage controllers or the storage controller interface  129   a ,  129   b  to the PCI bus  128   a ,  128   b.    
     A storage device controller  119 A-D may include mechanisms for implementing high availability primitives for use by other parts of a storage system external to the Dual PCI storage device  118 . For example, reservation or exclusion primitives may be provided so that, in a storage system with two storage controllers providing a highly available storage service, one storage controller may prevent the other storage controller from accessing or continuing to access the storage device. This could be used, for example, in cases where one controller detects that the other controller is not functioning properly or where the interconnect between the two storage controllers may itself not be functioning properly. 
     In one embodiment, a storage system for use with Dual PCI direct mapped storage devices with separately addressable fast write storage includes systems that manage erase blocks or groups of erase blocks as allocation units for storing data on behalf of the storage service, or for storing metadata (e.g., indexes, logs, etc.) associated with the storage service, or for proper management of the storage system itself. Flash pages, which may be a few kilobytes in size, may be written as data arrives or as the storage system is to persist data for long intervals of time (e.g., above a defined threshold of time). To commit data more quickly, or to reduce the number of writes to the Flash memory devices, the storage controllers may first write data into the separately addressable fast write storage on one more storage devices. 
     In one embodiment, the storage controllers  125   a ,  125   b  may initiate the use of erase blocks within and across storage devices (e.g.,  118 ) in accordance with an age and expected remaining lifespan of the storage devices, or based on other statistics. The storage controllers  125   a ,  125   b  may initiate garbage collection and data migration data between storage devices in accordance with pages that are no longer needed as well as to manage Flash page and erase block lifespans and to manage overall system performance. 
     In one embodiment, the storage system  124  may utilize mirroring and/or erasure coding schemes as part of storing data into addressable fast write storage and/or as part of writing data into allocation units associated with erase blocks. Erasure codes may be used across storage devices, as well as within erase blocks or allocation units, or within and across Flash memory devices on a single storage device, to provide redundancy against single or multiple storage device failures or to protect against internal corruptions of Flash memory pages resulting from Flash memory operations or from degradation of Flash memory cells. Mirroring and erasure coding at various levels may be used to recover from multiple types of failures that occur separately or in combination. 
     The embodiments depicted with reference to  FIGS. 2A-G  illustrate a storage cluster that stores user data, such as user data originating from one or more user or client systems or other sources external to the storage cluster. The storage cluster distributes user data across storage nodes housed within a chassis, or across multiple chassis, using erasure coding and redundant copies of metadata. Erasure coding refers to a method of data protection or reconstruction in which data is stored across a set of different locations, such as disks, storage nodes or geographic locations. Flash memory is one type of solid-state memory that may be integrated with the embodiments, although the embodiments may be extended to other types of solid-state memory or other storage medium, including non-solid state memory. Control of storage locations and workloads are distributed across the storage locations in a clustered peer-to-peer system. Tasks such as mediating communications between the various storage nodes, detecting when a storage node has become unavailable, and balancing I/Os (inputs and outputs) across the various storage nodes, are all handled on a distributed basis. Data is laid out or distributed across multiple storage nodes in data fragments or stripes that support data recovery in some embodiments. Ownership of data can be reassigned within a cluster, independent of input and output patterns. This architecture described in more detail below allows a storage node in the cluster to fail, with the system remaining operational, since the data can be reconstructed from other storage nodes and thus remain available for input and output operations. In various embodiments, a storage node may be referred to as a cluster node, a blade, or a server. 
     The storage cluster may be contained within a chassis, i.e., an enclosure housing one or more storage nodes. A mechanism to provide power to each storage node, such as a power distribution bus, and a communication mechanism, such as a communication bus that enables communication between the storage nodes are included within the chassis. The storage cluster can run as an independent system in one location according to some embodiments. In one embodiment, a chassis contains at least two instances of both the power distribution and the communication bus which may be enabled or disabled independently. The internal communication bus may be an Ethernet bus, however, other technologies such as PCIe, InfiniBand, and others, are equally suitable. The chassis provides a port for an external communication bus for enabling communication between multiple chassis, directly or through a switch, and with client systems. The external communication may use a technology such as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments, the external communication bus uses different communication bus technologies for inter-chassis and client communication. If a switch is deployed within or between chassis, the switch may act as a translation between multiple protocols or technologies. When multiple chassis are connected to define a storage cluster, the storage cluster may be accessed by a client using either proprietary interfaces or standard interfaces such as network file system (‘NFS’), common internet file system (‘CIFS’), small computer system interface (‘SCSI’) or hypertext transfer protocol (‘HTTP’). Translation from the client protocol may occur at the switch, chassis external communication bus or within each storage node. In some embodiments, multiple chassis may be coupled or connected to each other through an aggregator switch. A portion and/or all of the coupled or connected chassis may be designated as a storage cluster. As discussed above, each chassis can have multiple blades, each blade has a media access control (‘MAC’) address, but the storage cluster is presented to an external network as having a single cluster IP address and a single MAC address in some embodiments. 
     Each storage node may be one or more storage servers and each storage server is connected to one or more non-volatile solid state memory units, which may be referred to as storage units or storage devices. One embodiment includes a single storage server in each storage node and between one to eight non-volatile solid state memory units, however this one example is not meant to be limiting. The storage server may include a processor, DRAM and interfaces for the internal communication bus and power distribution for each of the power buses. Inside the storage node, the interfaces and storage unit share a communication bus, e.g., PCI Express, in some embodiments. The non-volatile solid state memory units may directly access the internal communication bus interface through a storage node communication bus, or request the storage node to access the bus interface. The non-volatile solid state memory unit contains an embedded CPU, solid state storage controller, and a quantity of solid state mass storage, e.g., between 2-32 terabytes (‘TB’) in some embodiments. An embedded volatile storage medium, such as DRAM, and an energy reserve apparatus are included in the non-volatile solid state memory unit. In some embodiments, the energy reserve apparatus is a capacitor, super-capacitor, or battery that enables transferring a subset of DRAM contents to a stable storage medium in the case of power loss. In some embodiments, the non-volatile solid state memory unit is constructed with a storage class memory, such as phase change or magnetoresistive random access memory (‘MRAM’) that substitutes for DRAM and enables a reduced power hold-up apparatus. 
     One of many features of the storage nodes and non-volatile solid state storage is the ability to proactively rebuild data in a storage cluster. The storage nodes and non-volatile solid state storage can determine when a storage node or non-volatile solid state storage in the storage cluster is unreachable, independent of whether there is an attempt to read data involving that storage node or non-volatile solid state storage. The storage nodes and non-volatile solid state storage then cooperate to recover and rebuild the data in at least partially new locations. This constitutes a proactive rebuild, in that the system rebuilds data without waiting until the data is needed for a read access initiated from a client system employing the storage cluster. These and further details of the storage memory and operation thereof are discussed below. 
       FIG. 2A  is a perspective view of a storage cluster  161 , with multiple storage nodes  150  and internal solid-state memory coupled to each storage node to provide network attached storage or storage area network, in accordance with some embodiments. A network attached storage, storage area network, or a storage cluster, or other storage memory, could include one or more storage clusters  161 , each having one or more storage nodes  150 , in a flexible and reconfigurable arrangement of both the physical components and the amount of storage memory provided thereby. The storage cluster  161  is designed to fit in a rack, and one or more racks can be set up and populated as desired for the storage memory. The storage cluster  161  has a chassis  138  having multiple slots  142 . It should be appreciated that chassis  138  may be referred to as a housing, enclosure, or rack unit. In one embodiment, the chassis  138  has fourteen slots  142 , although other numbers of slots are readily devised. For example, some embodiments have four slots, eight slots, sixteen slots, thirty-two slots, or other suitable number of slots. Each slot  142  can accommodate one storage node  150  in some embodiments. Chassis  138  includes flaps  148  that can be utilized to mount the chassis  138  on a rack. Fans  144  provide air circulation for cooling of the storage nodes  150  and components thereof, although other cooling components could be used, or an embodiment could be devised without cooling components. A switch fabric  146  couples storage nodes  150  within chassis  138  together and to a network for communication to the memory. In an embodiment depicted in herein, the slots  142  to the left of the switch fabric  146  and fans  144  are shown occupied by storage nodes  150 , while the slots  142  to the right of the switch fabric  146  and fans  144  are empty and available for insertion of storage node  150  for illustrative purposes. This configuration is one example, and one or more storage nodes  150  could occupy the slots  142  in various further arrangements. The storage node arrangements need not be sequential or adjacent in some embodiments. Storage nodes  150  are hot pluggable, meaning that a storage node  150  can be inserted into a slot  142  in the chassis  138 , or removed from a slot  142 , without stopping or powering down the system. Upon insertion or removal of storage node  150  from slot  142 , the system automatically reconfigures in order to recognize and adapt to the change. Reconfiguration, in some embodiments, includes restoring redundancy and/or rebalancing data or load. 
     Each storage node  150  can have multiple components. In the embodiment shown here, the storage node  150  includes a printed circuit board  159  populated by a CPU  156 , i.e., processor, a memory  154  coupled to the CPU  156 , and a non-volatile solid state storage  152  coupled to the CPU  156 , although other mountings and/or components could be used in further embodiments. The memory  154  has instructions which are executed by the CPU  156  and/or data operated on by the CPU  156 . As further explained below, the non-volatile solid state storage  152  includes flash or, in further embodiments, other types of solid-state memory. 
     Referring to  FIG. 2A , storage cluster  161  is scalable, meaning that storage capacity with non-uniform storage sizes is readily added, as described above. One or more storage nodes  150  can be plugged into or removed from each chassis and the storage cluster self-configures in some embodiments. Plug-in storage nodes  150 , whether installed in a chassis as delivered or later added, can have different sizes. For example, in one embodiment a storage node  150  can have any multiple of 4 TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, a storage node  150  could have any multiple of other storage amounts or capacities. Storage capacity of each storage node  150  is broadcast, and influences decisions of how to stripe the data. For maximum storage efficiency, an embodiment can self-configure as wide as possible in the stripe, subject to a predetermined requirement of continued operation with loss of up to one, or up to two, non-volatile solid state storage units  152  or storage nodes  150  within the chassis. 
       FIG. 2B  is a block diagram showing a communications interconnect  173  and power distribution bus  172  coupling multiple storage nodes  150 . Referring back to  FIG. 2A , the communications interconnect  173  can be included in or implemented with the switch fabric  146  in some embodiments. Where multiple storage clusters  161  occupy a rack, the communications interconnect  173  can be included in or implemented with a top of rack switch, in some embodiments. As illustrated in  FIG. 2B , storage cluster  161  is enclosed within a single chassis  138 . External port  176  is coupled to storage nodes  150  through communications interconnect  173 , while external port  174  is coupled directly to a storage node. External power port  178  is coupled to power distribution bus  172 . Storage nodes  150  may include varying amounts and differing capacities of non-volatile solid state storage  152  as described with reference to  FIG. 2A . In addition, one or more storage nodes  150  may be a compute only storage node as illustrated in  FIG. 2B . Authorities  168  are implemented on the non-volatile solid state storages  152 , for example as lists or other data structures stored in memory. In some embodiments the authorities are stored within the non-volatile solid state storage  152  and supported by software executing on a controller or other processor of the non-volatile solid state storage  152 . In a further embodiment, authorities  168  are implemented on the storage nodes  150 , for example as lists or other data structures stored in the memory  154  and supported by software executing on the CPU  156  of the storage node  150 . Authorities  168  control how and where data is stored in the non-volatile solid state storages  152  in some embodiments. This control assists in determining which type of erasure coding scheme is applied to the data, and which storage nodes  150  have which portions of the data. Each authority  168  may be assigned to a non-volatile solid state storage  152 . Each authority may control a range of inode numbers, segment numbers, or other data identifiers which are assigned to data by a file system, by the storage nodes  150 , or by the non-volatile solid state storage  152 , in various embodiments. 
     Every piece of data, and every piece of metadata, has redundancy in the system in some embodiments. In addition, every piece of data and every piece of metadata has an owner, which may be referred to as an authority. If that authority is unreachable, for example through failure of a storage node, there is a plan of succession for how to find that data or that metadata. In various embodiments, there are redundant copies of authorities  168 . Authorities  168  have a relationship to storage nodes  150  and non-volatile solid state storage  152  in some embodiments. Each authority  168 , covering a range of data segment numbers or other identifiers of the data, may be assigned to a specific non-volatile solid state storage  152 . In some embodiments the authorities  168  for all of such ranges are distributed over the non-volatile solid state storages  152  of a storage cluster. Each storage node  150  has a network port that provides access to the non-volatile solid state storage(s)  152  of that storage node  150 . Data can be stored in a segment, which is associated with a segment number and that segment number is an indirection for a configuration of a RAID (redundant array of independent disks) stripe in some embodiments. The assignment and use of the authorities  168  thus establishes an indirection to data. Indirection may be referred to as the ability to reference data indirectly, in this case via an authority  168 , in accordance with some embodiments. A segment identifies a set of non-volatile solid state storage  152  and a local identifier into the set of non-volatile solid state storage  152  that may contain data. In some embodiments, the local identifier is an offset into the device and may be reused sequentially by multiple segments. In other embodiments the local identifier is unique for a specific segment and never reused. The offsets in the non-volatile solid state storage  152  are applied to locating data for writing to or reading from the non-volatile solid state storage  152  (in the form of a RAID stripe). Data is striped across multiple units of non-volatile solid state storage  152 , which may include or be different from the non-volatile solid state storage  152  having the authority  168  for a particular data segment. 
     If there is a change in where a particular segment of data is located, e.g., during a data move or a data reconstruction, the authority  168  for that data segment should be consulted, at that non-volatile solid state storage  152  or storage node  150  having that authority  168 . In order to locate a particular piece of data, embodiments calculate a hash value for a data segment or apply an inode number or a data segment number. The output of this operation points to a non-volatile solid state storage  152  having the authority  168  for that particular piece of data. In some embodiments there are two stages to this operation. The first stage maps an entity identifier (ID), e.g., a segment number, inode number, or directory number to an authority identifier. This mapping may include a calculation such as a hash or a bit mask. The second stage is mapping the authority identifier to a particular non-volatile solid state storage  152 , which may be done through an explicit mapping. The operation is repeatable, so that when the calculation is performed, the result of the calculation repeatably and reliably points to a particular non-volatile solid state storage  152  having that authority  168 . The operation may include the set of reachable storage nodes as input. If the set of reachable non-volatile solid state storage units changes the optimal set changes. In some embodiments, the persisted value is the current assignment (which is always true) and the calculated value is the target assignment the cluster will attempt to reconfigure towards. This calculation may be used to determine the optimal non-volatile solid state storage  152  for an authority in the presence of a set of non-volatile solid state storage  152  that are reachable and constitute the same cluster. The calculation also determines an ordered set of peer non-volatile solid state storage  152  that will also record the authority to non-volatile solid state storage mapping so that the authority may be determined even if the assigned non-volatile solid state storage is unreachable. A duplicate or substitute authority  168  may be consulted if a specific authority  168  is unavailable in some embodiments. 
     With reference to  FIGS. 2A and 2B , two of the many tasks of the CPU  156  on a storage node  150  are to break up write data, and reassemble read data. When the system has determined that data is to be written, the authority  168  for that data is located as above. When the segment ID for data is already determined the request to write is forwarded to the non-volatile solid state storage  152  currently determined to be the host of the authority  168  determined from the segment. The host CPU  156  of the storage node  150 , on which the non-volatile solid state storage  152  and corresponding authority  168  reside, then breaks up or shards the data and transmits the data out to various non-volatile solid state storage  152 . The transmitted data is written as a data stripe in accordance with an erasure coding scheme. In some embodiments, data is requested to be pulled, and in other embodiments, data is pushed. In reverse, when data is read, the authority  168  for the segment ID containing the data is located as described above. The host CPU  156  of the storage node  150  on which the non-volatile solid state storage  152  and corresponding authority  168  reside requests the data from the non-volatile solid state storage and corresponding storage nodes pointed to by the authority. In some embodiments the data is read from flash storage as a data stripe. The host CPU  156  of storage node  150  then reassembles the read data, correcting any errors (if present) according to the appropriate erasure coding scheme, and forwards the reassembled data to the network. In further embodiments, some or all of these tasks can be handled in the non-volatile solid state storage  152 . In some embodiments, the segment host requests the data be sent to storage node  150  by requesting pages from storage and then sending the data to the storage node making the original request. 
     In 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 unit  152  may be assigned a range of address space. Within this assigned range, the non-volatile solid state storage  152  is able to allocate addresses without synchronization with other non-volatile solid state storage  152 . 
     Data and metadata is stored by a set of underlying storage layouts that are optimized for varying workload patterns and storage devices. These layouts incorporate multiple redundancy schemes, compression formats and index algorithms. Some of these layouts store information about authorities and authority masters, while others store file metadata and file data. The redundancy schemes include error correction codes that tolerate corrupted bits within a single storage device (such as a NAND flash chip), erasure codes that tolerate the failure of multiple storage nodes, and replication schemes that tolerate data center or regional failures. In some embodiments, low density parity check (‘LDPC’) code is used within a single storage unit. Reed-Solomon encoding is used within a storage cluster, and mirroring is used within a storage grid in some embodiments. Metadata may be stored using an ordered log structured index (such as a Log Structured Merge Tree), and large data may not be stored in a log structured layout. 
     In order to maintain consistency across multiple copies of an entity, the storage nodes agree implicitly on two things through calculations: (1) the authority that contains the entity, and (2) the storage node that contains the authority. The assignment of entities to authorities can be done by pseudo randomly assigning entities to authorities, by splitting entities into ranges based upon an externally produced key, or by placing a single entity into each authority. Examples of pseudorandom schemes are linear hashing and the Replication Under Scalable Hashing (‘RUSH’) family of hashes, including Controlled Replication Under Scalable Hashing (‘CRUSH’). In some embodiments, 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 storage units  152  described herein have multiple interfaces active simultaneously and serving multiple purposes. In some embodiments, some of the functionality of a storage node  150  is shifted into a storage unit  152 , transforming the storage unit  152  into a combination of storage unit  152  and storage node  150 . Placing computing (relative to storage data) into the storage unit  152  places this computing closer to the data itself. The various system embodiments have a hierarchy of storage node layers with different capabilities. By contrast, in a storage array, a controller owns and knows everything about all of the data that the controller manages in a shelf or storage devices. In a storage cluster  161 , as described herein, multiple controllers in multiple storage units  152  and/or storage nodes  150  cooperate in various ways (e.g., for erasure coding, data sharding, metadata communication and redundancy, storage capacity expansion or contraction, data recovery, and so on). 
       FIG. 2D  shows a storage server environment, which uses embodiments of the storage nodes  150  and storage units  152  of  FIGS. 2A-C . In this version, each storage unit  152  has a processor such as controller  212  (see  FIG. 2C ), an FPGA, flash memory  206 , and NVRAM  204  (which is super-capacitor backed DRAM  216 , see  FIGS. 2B and 2C ) on a PCIe (peripheral component interconnect express) board in a chassis  138  (see  FIG. 2A ). The storage unit  152  may be implemented as a single board containing storage, and may be the largest tolerable failure domain inside the chassis. In some embodiments, up to two storage units  152  may fail and the device will continue with no data loss. 
     The physical storage is divided into named regions based on application usage in some embodiments. The NVRAM  204  is a contiguous block of reserved memory in the storage unit  152  DRAM  216 , and is backed by NAND flash. NVRAM  204  is logically divided into multiple memory regions written for two as spool (e.g., spool_region). Space within the NVRAM  204  spools is managed by each authority  168  independently. Each device provides an amount of storage space to each authority  168 . That authority  168  further manages lifetimes and allocations within that space. Examples of a spool include distributed transactions or notions. When the primary power to a storage unit  152  fails, onboard super-capacitors provide a short duration of power hold up. During this holdup interval, the contents of the NVRAM  204  are flushed to flash memory  206 . On the next power-on, the contents of the NVRAM  204  are recovered from the flash memory  206 . 
     As for the storage unit controller, the responsibility of the logical “controller” is distributed across each of the blades containing authorities  168 . This distribution of logical control is shown in  FIG. 2D  as a host controller  242 , mid-tier controller  244  and storage unit controller(s)  246 . Management of the control plane and the storage plane are treated independently, although parts may be physically co-located on the same blade. Each authority  168  effectively serves as an independent controller. Each authority  168  provides its own data and metadata structures, its own background workers, and maintains its own lifecycle. 
       FIG. 2E  is a blade  252  hardware block diagram, showing a control plane  254 , compute and storage planes  256 ,  258 , and authorities  168  interacting with underlying physical resources, using embodiments of the storage nodes  150  and storage units  152  of  FIGS. 2A-C  in the storage server environment of  FIG. 2D . The control plane  254  is partitioned into a number of authorities  168  which can use the compute resources in the compute plane  256  to run on any of the blades  252 . The storage plane  258  is partitioned into a set of devices, each of which provides access to flash  206  and NVRAM  204  resources. In one embodiment, the compute plane  256  may perform the operations of a storage array controller, as described herein, on one or more devices of the storage plane  258  (e.g., a storage array). 
     In the compute and storage planes  256 ,  258  of  FIG. 2E , the authorities  168  interact with the underlying physical resources (i.e., devices). From the point of view of an authority  168 , its resources are striped over all of the physical devices. From the point of view of a device, it provides resources to all authorities  168 , irrespective of where the authorities happen to run. Each authority  168  has allocated or has been allocated one or more partitions  260  of storage memory in the storage units  152 , e.g. partitions  260  in flash memory  206  and NVRAM  204 . Each authority  168  uses those allocated partitions  260  that belong to it, for writing or reading user data. Authorities can be associated with differing amounts of physical storage of the system. For example, one authority  168  could have a larger number of partitions  260  or larger sized partitions  260  in one or more storage units  152  than one or more other authorities  168 . 
       FIG. 2F  depicts elasticity software layers in blades  252  of a storage cluster, in accordance with some embodiments. In the elasticity structure, elasticity software is symmetric, i.e., each blade&#39;s compute module  270  runs the three identical layers of processes depicted in  FIG. 2F . Storage managers  274  execute read and write requests from other blades  252  for data and metadata stored in local storage unit  152  NVRAM  204  and flash  206 . Authorities  168  fulfill client requests by issuing the necessary reads and writes to the blades  252  on whose storage units  152  the corresponding data or metadata resides. Endpoints  272  parse client connection requests received from switch fabric  146  supervisory software, relay the client connection requests to the authorities  168  responsible for fulfillment, and relay the authorities&#39;  168  responses to clients. The symmetric three-layer structure enables the storage system&#39;s high degree of concurrency. Elasticity scales out efficiently and reliably in these embodiments. In addition, elasticity implements a unique scale-out technique that balances work evenly across all resources regardless of client access pattern, and maximizes concurrency by eliminating much of the need for inter-blade coordination that typically occurs with conventional distributed locking. 
     Still referring to  FIG. 2F , authorities  168  running in the compute modules  270  of a blade  252  perform the internal operations required to fulfill client requests. One feature of elasticity is that authorities  168  are stateless, i.e., they cache active data and metadata in their own blades&#39;  252  DRAMs for fast access, but the authorities store every update in their NVRAM  204  partitions on three separate blades  252  until the update has been written to flash  206 . All the storage system writes to NVRAM  204  are in triplicate to partitions on three separate blades  252  in some embodiments. With triple-mirrored NVRAM  204  and persistent storage protected by parity and Reed-Solomon RAID checksums, the storage system can survive concurrent failure of two blades  252  with no loss of data, metadata, or access to either. 
     Because authorities  168  are stateless, they can migrate between blades  252 . Each authority  168  has a unique identifier. NVRAM  204  and flash  206  partitions are associated with authorities&#39;  168  identifiers, not with the blades  252  on which they are running in some. Thus, when an authority  168  migrates, the authority  168  continues to manage the same storage partitions from its new location. When a new blade  252  is installed in an embodiment of the storage cluster, the system automatically rebalances load by: partitioning the new blade&#39;s  252  storage for use by the system&#39;s authorities  168 , migrating selected authorities  168  to the new blade  252 , starting endpoints  272  on the new blade  252  and including them in the switch fabric&#39;s  146  client connection distribution algorithm. 
     From their new locations, migrated authorities  168  persist the contents of their NVRAM  204  partitions on flash  206 , process read and write requests from other authorities  168 , and fulfill the client requests that endpoints  272  direct to them. Similarly, if a blade  252  fails or is removed, the system redistributes its authorities  168  among the system&#39;s remaining blades  252 . The redistributed authorities  168  continue to perform their original functions from their new locations. 
       FIG. 2G  depicts authorities  168  and storage resources in blades  252  of a storage cluster, in accordance with some embodiments. Each authority  168  is exclusively responsible for a partition of the flash  206  and NVRAM  204  on each blade  252 . The authority  168  manages the content and integrity of its partitions independently of other authorities  168 . Authorities  168  compress incoming data and preserve it temporarily in their NVRAM  204  partitions, and then consolidate, RAID-protect, and persist the data in segments of the storage in their flash  206  partitions. As the authorities  168  write data to flash  206 , storage managers  274  perform the necessary flash translation to optimize write performance and maximize media longevity. In the background, authorities  168  “garbage collect,” or reclaim space occupied by data that clients have made obsolete by overwriting the data. It should be appreciated that since authorities&#39;  168  partitions are disjoint, there is no need for distributed locking to execute client and writes or to perform background functions. 
     The embodiments described herein may utilize various software, communication and/or networking protocols. In addition, the configuration of the hardware and/or software may be adjusted to accommodate various protocols. For example, the embodiments may utilize Active Directory, which is a database based system that provides authentication, directory, policy, and other services in a WINDOWS&#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 . The data communications link  304  may be embodied as a dedicated data communications link, as a data communications pathway that is provided through the use of one or data communications networks such as a wide area network (‘WAN’) or LAN, or as some other mechanism capable of transporting digital information between the storage system  306  and the cloud services provider  302 . Such a data communications link  304  may be fully wired, fully wireless, or some aggregation of wired and wireless data communications pathways. In such an example, digital information may be exchanged between the storage system  306  and the cloud services provider  302  via the data communications link  304  using one or more data communications protocols. For example, digital information may be exchanged between the storage system  306  and the cloud services provider  302  via the data communications link  304  using the handheld device transfer protocol (‘HDTP’), hypertext transfer protocol (‘HTTP’), internet protocol (‘IP’), real-time transfer protocol (‘RTP’), transmission control protocol (‘TCP’), user datagram protocol (‘UDP’), wireless application protocol (‘WAP’), or other protocol. 
     The cloud services provider  302  depicted in  FIG. 3A  may be embodied, for example, as a system and computing environment that provides a vast array of services to users of the cloud services provider  302  through the sharing of computing resources via the data communications link  304 . The cloud services provider  302  may provide on-demand access to a shared pool of configurable computing resources such as computer networks, servers, storage, applications and services, and so on. The shared pool of configurable resources may be rapidly provisioned and released to a user of the cloud services provider  302  with minimal management effort. Generally, the user of the cloud services provider  302  is unaware of the exact computing resources utilized by the cloud services provider  302  to provide the services. Although in many cases such a cloud services provider  302  may be accessible via the Internet, readers of skill in the art will recognize that any system that abstracts the use of shared resources to provide services to a user through any data communications link may be considered a cloud services provider  302 . 
     In the example depicted in  FIG. 3A , the cloud services provider  302  may be configured to provide a variety of services to the storage system  306  and users of the storage system  306  through the implementation of various service models. For example, the cloud services provider  302  may be configured to provide services through the implementation of an infrastructure as a service (‘IaaS’) service model, through the implementation of a platform as a service (‘PaaS’) service model, through the implementation of a software as a service (‘SaaS’) service model, through the implementation of an authentication as a service (‘AaaS’) service model, through the implementation of a storage as a service model where the cloud services provider  302  offers access to its storage infrastructure for use by the storage system  306  and users of the storage system  306 , and so on. Readers will appreciate that the cloud services provider  302  may be configured to provide additional services to the storage system  306  and users of the storage system  306  through the implementation of additional service models, as the service models described above are included only for explanatory purposes and in no way represent a limitation of the services that may be offered by the cloud services provider  302  or a limitation as to the service models that may be implemented by the cloud services provider  302 . 
     In the example depicted in  FIG. 3A , the cloud services provider  302  may be embodied, for example, as a private cloud, as a public cloud, or as a combination of a private cloud and public cloud. In an embodiment in which the cloud services provider  302  is embodied as a private cloud, the cloud services provider  302  may be dedicated to providing services to a single organization rather than providing services to multiple organizations. In an embodiment where the cloud services provider  302  is embodied as a public cloud, the cloud services provider  302  may provide services to multiple organizations. In still alternative embodiments, the cloud services provider  302  may be embodied as a mix of a private and public cloud services with a hybrid cloud deployment. 
     Although not explicitly depicted in  FIG. 3A , readers will appreciate that a vast amount of additional hardware components and additional software components may be necessary to facilitate the delivery of cloud services to the storage system  306  and users of the storage system  306 . For example, the storage system  306  may be coupled to (or even include) a cloud storage gateway. Such a cloud storage gateway may be embodied, for example, as hardware-based or software-based appliance that is located on premise with the storage system  306 . Such a cloud storage gateway may operate as a bridge between local applications that are executing on the storage array  306  and remote, cloud-based storage that is utilized by the storage array  306 . Through the use of a cloud storage gateway, organizations may move primary iSCSI or NAS to the cloud services provider  302 , thereby enabling the organization to save space on their on-premises storage systems. Such a cloud storage gateway may be configured to emulate a disk array, a block-based device, a file server, or other storage system that can translate the SCSI commands, file server commands, or other appropriate command into REST-space protocols that facilitate communications with the cloud services provider  302 . 
     In order to enable the storage system  306  and users of the storage system  306  to make use of the services provided by the cloud services provider  302 , a cloud migration process may take place during which data, applications, or other elements from an organization&#39;s local systems (or even from another cloud environment) are moved to the cloud services provider  302 . In order to successfully migrate data, applications, or other elements to the cloud services provider&#39;s  302  environment, middleware such as a cloud migration tool may be utilized to bridge gaps between the cloud services provider&#39;s  302  environment and an organization&#39;s environment. Such cloud migration tools may also be configured to address potentially high network costs and long transfer times associated with migrating large volumes of data to the cloud services provider  302 , as well as addressing security concerns associated with sensitive data to the cloud services provider  302  over data communications networks. In order to further enable the storage system  306  and users of the storage system  306  to make use of the services provided by the cloud services provider  302 , a cloud orchestrator may also be used to arrange and coordinate automated tasks in pursuit of creating a consolidated process or workflow. Such a cloud orchestrator may perform tasks such as configuring various components, whether those components are cloud components or on-premises components, as well as managing the interconnections between such components. The cloud orchestrator can simplify the inter-component communication and connections to ensure that links are correctly configured and maintained. 
     In the example depicted in  FIG. 3A , and as described briefly above, the cloud services provider  302  may be configured to provide services to the storage system  306  and users of the storage system  306  through the usage of a SaaS service model, eliminating the need to install and run the application on local computers, which may simplify maintenance and support of the application. Such applications may take many forms in accordance with various embodiments of the present disclosure. For example, the cloud services provider  302  may be configured to provide access to data analytics applications to the storage system  306  and users of the storage system  306 . Such data analytics applications may be configured, for example, to receive vast amounts of telemetry data phoned home by the storage system  306 . Such telemetry data may describe various operating characteristics of the storage system  306  and may be analyzed for a vast array of purposes including, for example, to determine the health of the storage system  306 , to identify workloads that are executing on the storage system  306 , to predict when the storage system  306  will run out of various resources, to recommend configuration changes, hardware or software upgrades, workflow migrations, or other actions that may improve the operation of the storage system  306 . 
     The cloud services provider  302  may also be configured to provide access to virtualized computing environments to the storage system  306  and users of the storage system  306 . Such virtualized computing environments may be embodied, for example, as a virtual machine or other virtualized computer hardware platforms, virtual storage devices, virtualized computer network resources, and so on. Examples of such virtualized environments can include virtual machines that are created to emulate an actual computer, virtualized desktop environments that separate a logical desktop from a physical machine, virtualized file systems that allow uniform access to different types of concrete file systems, and many others. 
     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 where data is initially written to storage resources with relatively fast write latencies, relatively high write bandwidth, or similar characteristics. In such an example, data that is written to the storage resources that serve as a write cache may later be written to other storage resources that may be characterized by slower write latencies, lower write bandwidth, or similar characteristics than the storage resources that are utilized to serve as a write cache. In a similar manner, storage resources within the storage system may be utilized as a read cache, where the read cache is populated in accordance with a set of predetermined rules or heuristics. In other embodiments, tiering may be achieved within the storage systems by placing data within the storage system in accordance with one or more policies such that, for example, data that is accessed frequently is stored in faster storage tiers while data that is accessed infrequently is stored in slower storage tiers. 
     The storage system  306  depicted in  FIG. 3B  also includes communications resources  310  that may be useful in facilitating data communications between components within the storage system  306 , as well as data communications between the storage system  306  and computing devices that are outside of the storage system  306 , including embodiments where those resources are separated by a relatively vast expanse. The communications resources  310  may be configured to utilize a variety of different protocols and data communication fabrics to facilitate data communications between components within the storage systems as well as computing devices that are outside of the storage system. For example, the communications resources  310  can include fibre channel (‘FC’) technologies such as FC fabrics and FC protocols that can transport SCSI commands over FC network, FC over ethernet (‘FCoE’) technologies through which FC frames are encapsulated and transmitted over Ethernet networks, InfiniBand (‘IB’) technologies in which a switched fabric topology is utilized to facilitate transmissions between channel adapters, NVM Express (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’) technologies through which non-volatile storage media attached via a PCI express (‘PCIe’) bus may be accessed, and others. In fact, the storage systems described above may, directly or indirectly, make use of neutrino communication technologies and devices through which information (including binary information) is transmitted using a beam of neutrinos. 
     The communications resources  310  can also include mechanisms for accessing storage resources  308  within the storage system  306  utilizing serial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces for connecting storage resources  308  within the storage system  306  to host bus adapters within the storage system  306 , internet small computer systems interface (‘iSCSI’) technologies to provide block-level access to storage resources  308  within the storage system  306 , and other communications resources that that may be useful in facilitating data communications between components within the storage system  306 , as well as data communications between the storage system  306  and computing devices that are outside of the storage system  306 . 
     The storage system  306  depicted in  FIG. 3B  also includes processing resources  312  that may be useful in useful in executing computer program instructions and performing other computational tasks within the storage system  306 . The processing resources  312  may include one or more ASICs that are customized for some particular purpose as well as one or more CPUs. The processing resources  312  may also include one or more DSPs, one or more FPGAs, one or more systems on a chip (‘SoCs’), or other form of processing resources  312 . The storage system  306  may utilize the storage resources  312  to perform a variety of tasks including, but not limited to, supporting the execution of software resources  314  that will be described in greater detail below. 
     The storage system  306  depicted in  FIG. 3B  also includes software resources  314  that, when executed by processing resources  312  within the storage system  306 , may perform a vast array of tasks. The software resources  314  may include, for example, one or more modules of computer program instructions that when executed by processing resources  312  within the storage system  306  are useful in carrying out various data protection techniques to preserve the integrity of data that is stored within the storage systems. Readers will appreciate that such data protection techniques may be carried out, for example, by system software executing on computer hardware within the storage system, by a cloud services provider, or in other ways. Such data protection techniques can include, for example, data archiving techniques that cause data that is no longer actively used to be moved to a separate storage device or separate storage system for long-term retention, data backup techniques through which data stored in the storage system may be copied and stored in a distinct location to avoid data loss in the event of equipment failure or some other form of catastrophe with the storage system, data replication techniques through which data stored in the storage system is replicated to another storage system such that the data may be accessible via multiple storage systems, data snapshotting techniques through which the state of data within the storage system is captured at various points in time, data and database cloning techniques through which duplicate copies of data and databases may be created, and other data protection techniques. 
     The software resources  314  may also include software that is useful in implementing software-defined storage (‘SDS’). In such an example, the software resources  314  may include one or more modules of computer program instructions that, when executed, are useful in policy-based provisioning and management of data storage that is independent of the underlying hardware. Such software resources  314  may be useful in implementing storage virtualization to separate the storage hardware from the software that manages the storage hardware. 
     The software resources  314  may also include software that is useful in facilitating and optimizing I/O operations that are directed to the storage resources  308  in the storage system  306 . For example, the software resources  314  may include software modules that perform carry out various data reduction techniques such as, for example, data compression, data deduplication, and others. The software resources  314  may include software modules that intelligently group together I/O operations to facilitate better usage of the underlying storage resource  308 , software modules that perform data migration operations to migrate from within a storage system, as well as software modules that perform other functions. Such software resources  314  may be embodied as one or more software containers or in many other ways. 
     For further explanation, the embodiments may be integrated into a cloud-based storage system. In this example, the cloud-based storage system is created entirely in a cloud computing environment such as, for example, Amazon Web Services (‘AWS’), Microsoft Azure, Google Cloud Platform, IBM Cloud, Oracle Cloud, and others. The cloud-based storage system may be used to provide services similar to the services that may be provided by the storage systems described above. For example, the cloud-based storage system may be used to provide block storage services to users of the cloud-based storage system, the cloud-based storage system may be used to provide storage services to users of the cloud-based storage system through the use of solid-state storage, and so on. 
     The cloud-based storage system may include two cloud computing instances that each are used to support the execution of a storage controller application. The cloud computing instances may be embodied, for example, as instances of cloud computing resources (e.g., virtual machines) that may be provided by the cloud computing environment to support the execution of software applications such as the storage controller application. In one embodiment, the cloud computing instances may be embodied as Amazon Elastic Compute Cloud (‘EC2’) instances. In such an example, an Amazon Machine Image (‘AMI’) that includes a storage controller application may be booted to create and configure a virtual machine that may execute the storage controller application. 
     A storage controller application may be embodied as a module of computer program instructions that, when executed, carries out various storage tasks. For example, the storage controller application 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 received from the users of the cloud-based storage system to the cloud-based storage system, erasing data from the cloud-based storage system, retrieving data from the cloud-based storage system and providing such data to users of the cloud-based storage system, monitoring and reporting of disk utilization and performance, performing redundancy operations, such as RAID or RAID-like data redundancy operations, compressing data, encrypting data, deduplicating data, and so forth. Readers will appreciate that there are two cloud computing instances each may include a storage controller application, in some embodiments one cloud computing instance may operate as the primary controller as described above while the other cloud computing instance may operate as the secondary controller as described above. Readers will appreciate that the storage controller application may include identical source code that is executed within different cloud computing instances. 
     Consider an example in which the cloud computing environment is embodied as AWS and the cloud computing instances are embodied as EC2 instances. In such an example, the cloud computing instance that operates as the primary controller may be deployed on one of the instance types that has a relatively large amount of memory and processing power while the cloud computing instance that operates as the secondary controller may be deployed on one of the instance types that has a relatively small amount of memory and processing power. In such an example, upon the occurrence of a failover event where the roles of primary and secondary are switched, a double failover may actually be carried out such that: 1) a first failover event where the cloud computing instance that formerly operated as the secondary controller begins to operate as the primary controller, and 2) a third cloud computing instance (not shown) that is of an instance type that has a relatively large amount of memory and processing power is spun up with a copy of the storage controller application, where the third cloud computing instance begins operating as the primary controller while the cloud computing instance that originally operated as the secondary controller begins operating as the secondary controller again. In such an example, the cloud computing instance that formerly operated as the primary controller may be terminated. Readers will appreciate that in alternative embodiments, the cloud computing instance that is operating as the secondary controller after the failover event may continue to operate as the secondary controller and the cloud computing instance that operated as the primary controller after the occurrence of the failover event may be terminated once the primary role has been assumed by the third cloud computing instance (not shown). 
     Readers will appreciate that while the embodiments described above relate to embodiments where one cloud computing instance operates as the primary controller and the second cloud computing instance operates as the secondary controller, other embodiments are within the scope of the present disclosure. For example, each cloud computing instance may operate as a primary controller for some portion of the address space supported by the cloud-based storage system, each cloud computing instance may operate as a primary controller where the servicing of I/O operations directed to the cloud-based storage system 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 may include cloud computing instances with local storage. The cloud computing instances may be embodied, for example, as instances of cloud computing resources that may be provided by the cloud computing environment to support the execution of software applications. The cloud computing instances may-have local storage-resources. The cloud computing instances with local storage may be embodied, for example, as EC2 M5 instances that include one or more SSDs, as EC2 R5 instances that include one or more SSDs, as EC2 I3 instances that include one or more SSDs, and so on. In some embodiments, the local storage must be embodied as solid-state storage (e.g., SSDs) rather than storage that makes use of hard disk drives. 
     Each of the cloud computing instances with local storage can include a software daemon that, when executed by a cloud computing instance can present itself to the storage controller applications as if the cloud computing instance were a physical storage device (e.g., one or more SSDs). In such an example, the software daemon may include computer program instructions similar to those that would normally be contained on a storage device such that the storage controller applications can send and receive the same commands that a storage controller would send to storage devices. In such a way, the storage controller applications 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 and the cloud computing instances with local storage may utilize iSCSI, NVMe over TCP, messaging, a custom protocol, or in some other mechanism. 
     Each of the cloud computing instances with local storage may also be coupled to block-storage that is offered by the cloud computing environment. The block-storage that is offered by the cloud computing environment may be embodied, for example, as Amazon Elastic Block Store (‘EBS’) volumes. For example, a first EBS volume may be coupled to a first cloud computing instance, a second EBS volume may be coupled to a second cloud computing instance, and a third EBS volume may be coupled to a third cloud computing instance. In such an example, the block-storage that is offered by the cloud computing environment may be utilized in a manner that is similar to how the NVRAM devices described above are utilized, as the software daemon (or some other module) that is executing within a particular cloud comping instance 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 resources. In some alternative embodiments, data may only be written to the local storage resources within a particular cloud comping instance. In an alternative embodiment, rather than using the block-storage that is offered by the cloud computing environment as NVRAM, actual RAM on each of the cloud computing instances with local storage may be used as NVRAM, thereby decreasing network utilization costs that would be associated with using an EBS volume as the NVRAM. 
     The cloud computing instances with local storage may be utilized, by cloud computing instances that support the execution of the storage controller application to service I/O operations that are directed to the cloud-based storage system. Consider an example in which a first cloud computing instance that is executing the storage controller application is operating as the primary controller. In such an example, the first cloud computing instance that is executing the storage controller application may receive (directly or indirectly via the secondary controller) requests to write data to the cloud-based storage system from users of the cloud-based storage system. In such an example, the first cloud computing instance that is executing the storage controller application may perform various tasks such as, for example, deduplicating the data contained in the request, compressing the data contained in the request, determining where to the write the data contained in the request, and so on, before ultimately sending a request to write a deduplicated, encrypted, or otherwise possibly updated version of the data to one or more of the cloud computing instances with local storage. Either cloud computing instance, in some embodiments, may receive a request to read data from the cloud-based storage system and may ultimately send a request to read data to one or more of the cloud computing instances with local storage. 
     Readers will appreciate that when a request to write data is received by a particular cloud computing instance with local storage the software daemon or some other module of computer program instructions that is executing on the particular cloud computing instance may be configured to not only write the data to its own local storage resources and any appropriate block-storage that are offered by the cloud computing environment, but the software daemon or some other module of computer program instructions that is executing on the particular cloud computing instance may also be configured to write the data to cloud-based object storage that is attached to the particular cloud computing instance. The cloud-based object storage that is attached to the particular cloud computing instance may be embodied, for example, as Amazon Simple Storage Service (‘S3’) storage that is accessible by the particular cloud computing instance. In other embodiments, the cloud computing instances that each include the storage controller application may initiate the storage of the data in the local storage of the cloud computing instances and the cloud-based object storage. 
     Readers will appreciate that, as described above, the cloud-based storage system may be used to provide block storage services to users of the cloud-based storage system. While the local storage resources and the block-storage resources that are utilized by the cloud computing instances may support block-level access, the cloud-based object storage that is attached to the particular cloud computing instance supports only object-based access. In order to address this, the software daemon or some other module of computer program instructions that is executing on the particular cloud computing instance may be configured to take blocks of data, package those blocks into objects, and write the objects to the cloud-based object storage that is attached to the particular cloud computing instance. 
     Consider an example in which data is written to the local storage resources and the block-storage resources that are utilized by the cloud computing instances in 1 MB blocks. In such an example, assume that a user of the cloud-based storage system issues a request to write data that, after being compressed and deduplicated by the storage controller application results in the need to write 5 MB of data. In such an example, writing the data to the local storage resources and the block-storage resources that are utilized by the cloud computing instances is relatively straightforward as 5 blocks that are 1 MB in size are written to the local storage resources and the block-storage resources that are utilized by the cloud computing instances. In such an example, the software daemon or some other module of computer program instructions that is executing on the particular cloud computing instance may be configured to: 1) create a first object that includes the first 1 MB of data and write the first object to the cloud-based object storage, 2) create a second object that includes the second 1 MB of data and write the second object to the cloud-based object storage, 3) create a third object that includes the third 1 MB of data and write the third object to the cloud-based object storage, and so on. As such, in some embodiments, each object that is written to the cloud-based object storage may be identical (or nearly identical) in size. Readers will appreciate that in such an example, metadata that is associated with the data itself may be included in each object (e.g., the first 1 MB of the object is data and the remaining portion is metadata associated with the data). 
     Readers will appreciate that the cloud-based object storage may be incorporated into the cloud-based storage system to increase the durability of the cloud-based storage system. Continuing with the example described above where the cloud computing instances are EC2 instances, readers will understand that EC2 instances are only guaranteed to have a monthly uptime of 99.9% and data stored in the local instance store only persists during the lifetime of the EC2 instance. As such, relying on the cloud computing instances with local storage as the only source of persistent data storage in the cloud-based storage system may result in a relatively unreliable storage system. Likewise, EBS volumes are designed for 99.999% availability. As such, even relying on EBS as the persistent data store in the cloud-based storage system may result in a storage system that is not sufficiently durable. Amazon S3, however, is designed to provide 99.999999999% durability, meaning that a cloud-based storage system that can incorporate S3 into its pool of storage is substantially more durable than various other options. 
     Readers will appreciate that while a cloud-based storage system that can incorporate S3 into its pool of storage is substantially more durable than various other options, utilizing S3 as the primary pool of storage may result in storage system that has relatively slow response times and relatively long I/O latencies. As such, the cloud-based storage system may not only stores data in S3 but the cloud-based storage system also stores data in local storage resources and block-storage resources that are utilized by the cloud computing instances, such that read operations can be serviced from local storage resources and the block-storage resources that are utilized by the cloud computing instances, thereby reducing read latency when users of the cloud-based storage system attempt to read data from the cloud-based storage system. 
     In some embodiments, all data that is stored by the cloud-based storage system may be stored in both: 1) the cloud-based object storage, and 2) at least one of the local storage resources or block-storage resources that are utilized by the cloud computing instances. In such embodiments, the local storage resources and block-storage resources that are utilized by the cloud computing instances 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 without requiring the cloud computing instances to access the cloud-based object storage. Readers will appreciate that in other embodiments, however, all data that is stored by the cloud-based storage system may be stored in the cloud-based object storage, but less than all data that is stored by the cloud-based storage system may be stored in at least one of the local storage resources or block-storage resources that are utilized by the cloud computing instances. 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 should reside in both: 1) the cloud-based object storage, and 2) at least one of the local storage resources or block-storage resources that are utilized by the cloud computing instances. 
     As described above, when the cloud computing instances with local storage are embodied as EC2 instances, the cloud computing instances with local storage are only guaranteed to have a monthly uptime of 99.9% and data stored in the local instance store only persists during the lifetime of each cloud computing instance with local storage. As such, one or more modules of computer program instructions that are executing within the cloud-based storage system (e.g., a monitoring module that is executing on its own EC2 instance) may be designed to handle the failure of one or more of the cloud computing instances with local storage. In such an example, the monitoring module may handle the failure of one or more of the cloud computing instances with local storage by creating one or more new cloud computing instances with local storage, retrieving data that was stored on the failed cloud computing instances from the cloud-based object storage, and storing the data retrieved from the cloud-based object storage in local storage on the newly created cloud computing instances. Readers will appreciate that many variants of this process may be implemented. 
     Consider an example in which all cloud computing instances with local storage failed. In such an example, the monitoring module may create new cloud computing instances with local storage, where high-bandwidth instances types are selected that allow for the maximum data transfer rates between the newly created high-bandwidth cloud computing instances with local storage and the cloud-based object storage. Readers will appreciate that instances types are selected that allow for the maximum data transfer rates between the new cloud computing instances and the cloud-based object storage such that the new high-bandwidth cloud computing instances can be rehydrated with data from the cloud-based object storage as quickly as possible. Once the new high-bandwidth cloud computing instances are rehydrated with data from the cloud-based object storage, less expensive lower-bandwidth cloud computing instances may be created, data may be migrated to the less expensive lower-bandwidth cloud computing instances, and the high-bandwidth cloud computing instances may be terminated. 
     Readers will appreciate that in some embodiments, the number of new cloud computing instances that are created may substantially exceed the number of cloud computing instances that are needed to locally store all of the data stored by the cloud-based storage system. The number of new cloud computing instances that are created may substantially exceed the number of cloud computing instances that are needed to locally store all of the data stored by the cloud-based storage system in order to more rapidly pull data from the cloud-based object storage and into the new cloud computing instances, as each new cloud computing instance can (in parallel) retrieve some portion of the data stored by the cloud-based storage system. In such embodiments, once the data stored by the cloud-based storage system has been pulled into the newly created cloud computing instances, the data may be consolidated within a subset of the newly created cloud computing instances and those newly created cloud computing instances that are excessive may be terminated. 
     Consider an example in which 1000 cloud computing instances are needed in order to locally store all valid data that users of the cloud-based storage system have written to the cloud-based storage system. In such an example, assume that all 1,000 cloud computing instances fail. In such an example, the monitoring module may cause 100,000 cloud computing instances to be created, where each cloud computing instance is responsible for retrieving, from the cloud-based object storage, distinct 1/100,000th chunks of the valid data that users of the cloud-based storage system have written to the cloud-based storage system and locally storing the distinct chunk of the dataset that it retrieved. In such an example, because each of the 100,000 cloud computing instances can retrieve data from the cloud-based object storage in parallel, the caching layer may be restored 100 times faster as compared to an embodiment where the monitoring module only create 1000 replacement cloud computing instances. In such an example, over time the data that is stored locally in the 100,000 could be consolidated into 1,000 cloud computing instances and the remaining 99,000 cloud computing instances could be terminated. 
     Readers will appreciate that various performance aspects of the cloud-based storage system may be monitored (e.g., by a monitoring module that is executing in an EC2 instance) such that the cloud-based storage system can be scaled-up or scaled-out as needed. Consider an example in which the monitoring module monitors the performance of the could-based storage system via communications with one or more of the cloud computing instances that each are used to support the execution of a storage controller application via monitoring communications between cloud computing instances, via monitoring communications between cloud computing instances and the cloud-based object storage, or in some other way. In such an example, assume that the monitoring module determines that the cloud computing instances that are used to support the execution of a storage controller application are undersized and not sufficiently servicing the I/O requests that are issued by users of the cloud-based storage system. In such an example, the monitoring module may create a new, more powerful cloud computing instance (e.g., a cloud computing instance of a type that includes more processing power, more memory, etc. . . . ) that includes the storage controller application such that the new, more powerful cloud computing instance can begin operating as the primary controller. Likewise, if the monitoring module determines that the cloud computing instances that are used to support the execution of a storage controller application are oversized and that cost savings could be gained by switching to a smaller, less powerful cloud computing instance, the monitoring module may create a new, less powerful (and less expensive) cloud computing instance that includes the storage controller application such that the new, less powerful cloud computing instance can begin operating as the primary controller. 
     Consider, as an additional example of dynamically sizing the cloud-based storage system, an example in which the monitoring module determines that the utilization of the local storage that is collectively provided by the cloud computing instances has reached a predetermined utilization threshold (e.g., 95%). In such an example, the monitoring module may create additional cloud computing instances with local storage to expand the pool of local storage that is offered by the cloud computing instances. Alternatively, the monitoring module may create one or more new cloud computing instances that have larger amounts of local storage than the already existing cloud computing instances, such that data stored in an already existing cloud computing instance can be migrated to the one or more new cloud computing instances and the already existing cloud computing instance can be terminated, thereby expanding the pool of local storage that is offered by the cloud computing instances. Likewise, if the pool of local storage that is offered by the cloud computing instances is unnecessarily large, data can be consolidated and some cloud computing instances can be terminated. 
     Readers will appreciate that the cloud-based storage system may be sized up and down automatically by a monitoring module applying a predetermined set of rules that may be relatively simple of relatively complicated. In fact, the monitoring module may not only take into account the current state of the cloud-based storage system, but the monitoring module may also apply predictive policies that are based on, for example, observed behavior (e.g., every night from 10 PM until 6 AM usage of the storage system is relatively light), predetermined fingerprints (e.g., every time a virtual desktop infrastructure adds 100 virtual desktops, the number of IOPS directed to the storage system increase by X), and so on. In such an example, the dynamic scaling of the cloud-based storage system may be based on current performance metrics, predicted workloads, and many other factors, including combinations thereof. 
     Readers will further appreciate that because the cloud-based storage system may be dynamically scaled, the cloud-based storage system may even operate in a way that is more dynamic. Consider the example of garbage collection. In a traditional storage system, the amount of storage is fixed. As such, at some point the storage system may be forced to perform garbage collection as the amount of available storage has become so constrained that the storage system is on the verge of running out of storage. In contrast, the cloud-based storage system described here can always ‘add’ additional storage (e.g., by adding more cloud computing instances with local storage). Because the cloud-based storage system described here can always ‘add’ additional storage, the cloud-based storage system can make more intelligent decisions regarding when to perform garbage collection. For example, the cloud-based storage system may implement a policy that garbage collection only be performed when the number of TOPS being serviced by the cloud-based storage system falls below a certain level. In some embodiments, other system-level functions (e.g., deduplication, compression) may also be turned off and on in response to system load, given that the size of the cloud-based storage system is not constrained in the same way that traditional storage systems are constrained. 
     Readers will appreciate that embodiments of the present disclosure resolve an issue with block-storage services offered by some cloud computing environments as some cloud computing environments only allow for one cloud computing instance to connect to a block-storage volume at a single time. For example, in Amazon AWS, only a single EC2 instance may be connected to an EBS volume. Through the use of EC2 instances with local storage, embodiments of the present disclosure can offer multi-connect capabilities where multiple EC2 instances can connect to another EC2 instance with local storage (‘a drive instance’). In such embodiments, the drive instances may include software executing within the drive instance that allows the drive instance to support I/O directed to a particular volume from each connected EC2 instance. As such, some embodiments of the present disclosure may be embodied as multi-connect block storage services. 
     In some embodiments, especially in embodiments where the cloud-based object storage resources are embodied as Amazon S3, the cloud-based storage system may include one or more modules (e.g., a module of computer program instructions executing on an EC2 instance) that are configured to ensure that when the local storage of a particular cloud computing instance is rehydrated with data from S3, the appropriate data is actually in S3. This issue arises largely because S3 implements an eventual consistency model where, when overwriting an existing object, reads of the object will eventually (but not necessarily immediately) become consistent and will eventually (but not necessarily immediately) return the overwritten version of the object. To address this issue, in some embodiments of the present disclosure, objects in S3 are never overwritten. Instead, a traditional ‘overwrite’ would result in the creation of the new object (that includes the updated version of the data) and the eventual deletion of the old object (that includes the previous version of the data). 
     In some embodiments of the present disclosure, as part of an attempt to never (or almost never) overwrite an object, when data is written to S3 the resultant object may be tagged with a sequence number. In some embodiments, these sequence numbers may be persisted elsewhere (e.g., in a database) such that at any point in time, the sequence number associated with the most up-to-date version of some piece of data can be known. In such a way, a determination can be made as to whether S3 has the most recent version of some piece of data by merely reading the sequence number associated with an object—and without actually reading the data from S3. The ability to make this determination may be particularly important when a cloud computing instance with local storage crashes, as it would be undesirable to rehydrate the local storage of a replacement cloud computing instance with out-of-date data. In fact, because the cloud-based storage system does not need to access the data to verify its validity, the data can stay encrypted and access charges can be avoided. 
     The storage systems described above may carry out intelligent data backup techniques through which data stored in the storage system may be copied and stored in a distinct location to avoid data loss in the event of equipment failure or some other form of catastrophe. For example, the storage systems described above may be configured to examine each backup to avoid restoring the storage system to an undesirable state. Consider an example in which malware infects the storage system. In such an example, the storage system may include software resources  314  that can scan each backup to identify backups that were captured before the malware infected the storage system and those backups that were captured after the malware infected the storage system. In such an example, the storage system may restore itself from a backup that does not include the malware—or at least not restore the portions of a backup that contained the malware. In such an example, the storage system may include software resources  314  that can scan each backup to identify the presences of malware (or a virus, or some other undesirable), for example, by identifying write operations that were serviced by the storage system and originated from a network subnet that is suspected to have delivered the malware, by identifying write operations that were serviced by the storage system and originated from a user that is suspected to have delivered the malware, by identifying write operations that were serviced by the storage system and examining the content of the write operation against fingerprints of the malware, and in many other ways. 
     Readers will further appreciate that the backups (often in the form of one or more snapshots) may also be utilized to perform rapid recovery of the storage system. Consider an example in which the storage system is infected with ransomware that locks users out of the storage system. In such an example, software resources  314  within the storage system may be configured to detect the presence of ransomware and may be further configured to restore the storage system to a point-in-time, using the retained backups, prior to the point-in-time at which the ransomware infected the storage system. In such an example, the presence of ransomware may be explicitly detected through the use of software tools utilized by the system, through the use of a key (e.g., a USB drive) that is inserted into the storage system, or in a similar way. Likewise, the presence of ransomware may be inferred in response to system activity meeting a predetermined fingerprint such as, for example, no reads or writes coming into the system for a predetermined period of time. 
     Readers will appreciate that the various components described above may be grouped into one or more optimized computing packages as converged infrastructures. Such converged infrastructures may include pools of computers, storage and networking resources that can be shared by multiple applications and managed in a collective manner using policy-driven processes. Such converged infrastructures may be implemented with a converged infrastructure reference architecture, with standalone appliances, with a software driven hyper-converged approach (e.g., hyper-converged infrastructures), or in other ways. 
     Readers will appreciate that the storage systems described above may be useful for supporting various types of software applications. For example, the storage system  306  may be useful in supporting artificial intelligence (‘AI’) applications, database applications, DevOps projects, electronic design automation tools, event-driven software applications, high performance computing applications, simulation applications, high-speed data capture and analysis applications, machine learning applications, media production applications, media serving applications, picture archiving and communication systems (‘PACS’) applications, software development applications, virtual reality applications, augmented reality applications, and many other types of applications by providing storage resources to such applications. 
     The storage systems described above may operate to support a wide variety of applications. In view of the fact that the storage systems include compute resources, storage resources, and a wide variety of other resources, the storage systems may be well suited to support applications that are resource intensive such as, for example, AI applications. AI applications may be deployed in a variety of fields, including: predictive maintenance in manufacturing and related fields, healthcare applications such as patient data &amp; risk analytics, retail and marketing deployments (e.g., search advertising, social media advertising), supply chains solutions, fintech solutions such as business analytics &amp; reporting tools, operational deployments such as real-time analytics tools, application performance management tools, IT infrastructure management tools, and many others. 
     Such AI applications may enable devices to perceive their environment and take actions that maximize their chance of success at some goal. Examples of such AI applications can include IBM Watson, Microsoft Oxford, Google DeepMind, Baidu Minwa, and others. The storage systems described above may also be well suited to support other types of applications that are resource intensive such as, for example, machine learning applications. Machine learning applications may perform various types of data analysis to automate analytical model building. Using algorithms that iteratively learn from data, machine learning applications can enable computers to learn without being explicitly programmed. One particular area of machine learning is referred to as reinforcement learning, which involves taking suitable actions to maximize reward in a particular situation. Reinforcement learning may be employed to find the best possible behavior or path that a particular software application or machine should take in a specific situation. Reinforcement learning differs from other areas of machine learning (e.g., supervised learning, unsupervised learning) in that correct input/output pairs need not be presented for reinforcement learning and sub-optimal actions need not be explicitly corrected. 
     In addition to the resources already described, the storage systems described above may also include graphics processing units (‘GPUs’), occasionally referred to as visual processing unit (‘VPUs’). Such GPUs may be embodied as specialized electronic circuits that rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. Such GPUs may be included within any of the computing devices that are part of the storage systems described above, including as one of many individually scalable components of a storage system, where other examples of individually scalable components of such storage system can include storage components, memory components, compute components (e.g., CPUs, FPGAs, ASICs), networking components, software components, and others. In addition to GPUs, the storage systems described above may also include neural network processors (‘NNPs’) for use in various aspects of neural network processing. Such NNPs may be used in place of (or in addition to) GPUs and may be also be independently scalable. 
     As described above, the storage systems described herein may be configured to support artificial intelligence applications, machine learning applications, big data analytics applications, and many other types of applications. The rapid growth in these sort of applications is being driven by three technologies: deep learning (DL), GPU processors, and Big Data. Deep learning is a computing model that makes use of massively parallel neural networks inspired by the human brain. Instead of experts handcrafting software, a deep learning model writes its own software by learning from lots of examples. Such GPUs may include thousands of cores that are well-suited to run algorithms that loosely represent the parallel nature of the human brain. 
     Advances in deep neural networks, 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 such techniques may include: machine and vehicular object detection, identification and avoidance; visual recognition, classification and tagging; algorithmic financial trading strategy performance management; simultaneous localization and mapping; predictive maintenance of high-value machinery; prevention against cyber security threats, expertise automation; image recognition and classification; question answering; robotics; text analytics (extraction, classification) and text generation and translation; and many others. Applications of AI techniques has materialized in a wide array of products include, for example, Amazon Echo&#39;s speech recognition technology that allows users to talk to their machines, Google Translate™ which allows for machine-based language translation, Spotify&#39;s Discover Weekly that provides recommendations on new songs and artists that a user may like based on the user&#39;s usage and traffic analysis, Quill&#39;s text generation offering that takes structured data and turns it into narrative stories, Chatbots that provide real-time, contextually specific answers to questions in a dialog format, and many others. 
     Data is the heart of modern AI and deep learning algorithms. Before training can begin, one problem that must be addressed revolves around collecting the labeled data that is crucial for training an accurate AI model. A full scale AI deployment may be required to continuously collect, clean, transform, label, and store large amounts of data. Adding additional high quality data points directly translates to more accurate models and better insights. Data samples may undergo a series of processing steps including, but not limited to: 1) ingesting the data from an external source into the training system and storing the data in raw form, 2) cleaning and transforming the data in a format convenient for training, including linking data samples to the appropriate label, 3) exploring parameters and models, quickly testing with a smaller dataset, and iterating to converge on the most promising models to push into the production cluster, 4) executing training phases to select random batches of input data, including both new and older samples, and feeding those into production GPU servers for computation to update model parameters, and 5) evaluating including using a holdback portion of the data not used in training in order to evaluate model accuracy on the holdout data. This lifecycle may apply for any type of parallelized machine learning, not just neural networks or deep learning. For example, standard machine learning frameworks may rely on CPUs instead of GPUs but the data ingest and training workflows may be the same. Readers will appreciate that a single shared storage data hub creates a coordination point throughout the lifecycle without the need for extra data copies among the ingest, preprocessing, and training stages. Rarely is the ingested data used for only one purpose, and shared storage gives the flexibility to train multiple different models or apply traditional analytics to the data. 
     Readers will appreciate that each stage in the AI data pipeline may have varying requirements from the data hub (e.g., the storage system or collection of storage systems). Scale-out storage systems must deliver uncompromising performance for all manner of access types and patterns—from small, metadata-heavy to large files, from random to sequential access patterns, and from low to high concurrency. The storage systems described above may serve as an ideal AI data hub as the systems may service unstructured workloads. In the first stage, data is ideally ingested and stored on to the same data hub that following stages will use, in order to avoid excess data copying. The next two steps can be done on a standard compute server that optionally includes a GPU, and then in the fourth and last stage, full training production jobs are run on powerful GPU-accelerated servers. Often, there is a production pipeline alongside an experimental pipeline operating on the same dataset. Further, the GPU-accelerated servers can be used independently for different models or joined together to train on one larger model, even spanning multiple systems for distributed training. If the shared storage tier is slow, then data must be copied to local storage for each phase, resulting in wasted time staging data onto different servers. The ideal data hub for the AI training pipeline delivers performance similar to data stored locally on the server node while also having the simplicity and performance to enable all pipeline stages to operate concurrently. 
     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. Readers will appreciate that in embodiments where the storage systems are embodied as cloud-based storage systems as described below, virtual drive or other components within such a cloud-based storage system may also be configured 
     Although the preceding paragraphs discuss deep learning applications, readers will appreciate that the storage systems described herein may also be part of a distributed deep learning (‘DDL’) platform to support the execution of DDL algorithms. The storage systems described above may also be paired with other technologies such as TensorFlow, an open-source software library for dataflow programming across a range of tasks that may be used for machine learning applications such as neural networks, to facilitate the development of such machine learning models, applications, and so on. 
     The storage systems described above may also be used in a neuromorphic computing environment. Neuromorphic computing is a form of computing that mimics brain cells. To support neuromorphic computing, an architecture of interconnected “neurons” replace traditional computing models with low-powered signals that go directly between neurons for more efficient computation. Neuromorphic computing may make use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system, as well as analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems for perception, motor control, or multisensory integration. 
     Readers will appreciate that the storage systems described above may be configured to support the storage or use of (among other types of data) blockchains. In addition to supporting the storage and use of blockchain technologies, the storage systems described above may also support the storage and use of derivative items such as, for example, open source blockchains and related tools that are part of the IBM™ Hyperledger project, permissioned blockchains in which a certain number of trusted parties are allowed to access the block chain, blockchain products that enable developers to build their own distributed ledger projects, and others. Blockchains and the storage systems described herein may be leveraged to support on-chain storage of data as well as off-chain storage of data. 
     Off-chain storage of data can be implemented in a variety of ways and can occur when the data itself is not stored within the blockchain. For example, in one embodiment, a hash function may be utilized and the data itself may be fed into the hash function to generate a hash value. In such an example, the hashes of large pieces of data may be embedded within transactions, instead of the data itself. Readers will appreciate that, in other embodiments, alternatives to blockchains may be used to facilitate the decentralized storage of information. For example, one alternative to a blockchain that may be used is a blockweave. While conventional blockchains store every transaction to achieve validation, a blockweave permits secure decentralization without the usage of the entire chain, thereby enabling low cost on-chain storage of data. Such blockweaves may utilize a consensus mechanism that is based on proof of access (PoA) and proof of work (PoW). 
     The storage systems described above may, either alone or in combination with other computing devices, be used to support in-memory computing applications. In-memory computing involves the storage of information in RAM that is distributed across a cluster of computers. Readers will appreciate that the storage systems described above, especially those that are configurable with customizable amounts of processing resources, storage resources, and memory resources (e.g., those systems in which blades that contain configurable amounts of each type of resource), may be configured in a way so as to provide an infrastructure that can support in-memory computing. Likewise, the storage systems described above may include component parts (e.g., NVDIMMs, 3D crosspoint storage that provide fast random access memory that is persistent) that can actually provide for an improved in-memory computing environment as compared to in-memory computing environments that rely on RAM distributed across dedicated servers. 
     In some embodiments, the storage systems described above may be configured to operate as a hybrid in-memory computing environment that includes a universal interface to all storage media (e.g., RAM, flash storage, 3D crosspoint storage). In such embodiments, users may have no knowledge regarding the details of where their data is stored but they can still use the same full, unified API to address data. In such embodiments, the storage system may (in the background) move data to the fastest layer available—including intelligently placing the data in dependence upon various characteristics of the data or in dependence upon some other heuristic. In such an example, the storage systems may even make use of existing products such as Apache Ignite and GridGain to move data between the various storage layers, or the storage systems may make use of custom software to move data between the various storage layers. The storage systems described herein may implement various optimizations to improve the performance of in-memory computing such as, for example, having computations occur as close to the data as possible. 
     Readers will further appreciate that in some embodiments, the storage systems described above may be paired with other resources to support the applications described above. For example, one infrastructure could include primary compute in the form of servers and workstations which specialize in using General-purpose computing on graphics processing units (‘GPGPU’) to accelerate deep learning applications that are interconnected into a computation engine to train parameters for deep neural networks. Each system may have Ethernet external connectivity, InfiniBand external connectivity, some other form of external connectivity, or some combination thereof. In such an example, the GPUs can be grouped for a single large training or used independently to train multiple models. The infrastructure could also include a storage system such as those described above to provide, for example, a scale-out all-flash file or object store through which data can be accessed via high-performance protocols such as NFS, S3, and so on. The infrastructure can also include, for example, redundant top-of-rack Ethernet switches connected to storage and compute via ports in MLAG port channels for redundancy. The infrastructure could also include additional compute in the form of whitebox servers, optionally with GPUs, for data ingestion, pre-processing, and model debugging. Readers will appreciate that additional infrastructures are also be possible. 
     Readers will appreciate that the storage systems described above, either alone or in coordination with other computing machinery may be configured to support other AI related tools. For example, the storage systems may make use of tools like ONXX or other open neural network exchange formats that make it easier to transfer models written in different AI frameworks. Likewise, the storage systems may be configured to support tools like Amazon&#39;s Gluon that allow developers to prototype, build, and train deep learning models. In fact, the storage systems described above may be part of a larger platform, such as IBM™ Cloud Private for Data, that includes integrated data science, data engineering and application building services. 
     Readers will further appreciate that the storage systems described above may also be deployed as an edge solution. Such an edge solution may be in place to optimize cloud computing systems by performing data processing at the edge of the network, near the source of the data. Edge computing can push applications, data and computing power (i.e., services) away from centralized points to the logical extremes of a network. Through the use of edge solutions such as the storage systems described above, computational tasks may be performed using the compute resources provided by such storage systems, data may be storage using the storage resources of the storage system, and cloud-based services may be accessed through the use of various resources of the storage system (including networking resources). By performing computational tasks on the edge solution, storing data on the edge solution, and generally making use of the edge solution, the consumption of expensive cloud-based resources may be avoided and, in fact, performance improvements may be experienced relative to a heavier reliance on cloud-based resources. 
     While many tasks may benefit from the utilization of an edge solution, some particular uses may be especially suited for deployment in such an environment. For example, devices like drones, autonomous cars, robots, and others may require extremely rapid processing so fast, in fact, that sending data up to a cloud environment and back to receive data processing support may simply be too slow. As an additional example, some IoT devices such as connected video cameras may not be well-suited for the utilization of cloud-based resources as it may be impractical (not only from a privacy perspective, security perspective, or a financial perspective) to send the data to the cloud simply because of the pure volume of data that is involved. As such, many tasks that really on data processing, storage, or communications may be better suited by platforms that include edge solutions such as the storage systems described above. 
     The storage systems described above may alone, or in combination with other computing resources, serves as a network edge platform that combines compute resources, storage resources, networking resources, cloud technologies and network virtualization technologies, and so on. As part of the network, the edge may take on characteristics similar to other network facilities, from the customer premise and backhaul aggregation facilities to Points of Presence (PoPs) and regional data centers. Readers will appreciate that network workloads, such as Virtual Network Functions (VNFs) and others, will reside on the network edge platform. Enabled by a combination of containers and virtual machines, the network edge platform may rely on controllers and schedulers that are no longer geographically co-located with the data processing resources. The functions, as microservices, may split into control planes, user and data planes, or even state machines, allowing for independent optimization and scaling techniques to be applied. Such user and data planes may be enabled through increased accelerators, both those residing in server platforms, such as FPGAs and Smart NICs, and through SDN-enabled merchant silicon and programmable ASICs. 
     The storage systems described above may also be optimized for use in big data analytics. Big data analytics may be generally described as the process of examining large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. As part of that process, semi-structured and unstructured data such as, for example, internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile-phone call-detail records, IoT sensor data, and other data may be converted to a structured form. 
     The storage systems described above may also support (including implementing as a system interface) applications that perform tasks in response to human speech. For example, the storage systems may support the execution intelligent personal assistant applications such as, for example, Amazon&#39;s Alexa, Apple Siri, Google Voice, Samsung Bixby, Microsoft Cortana, and others. While the examples described in the previous sentence make use of voice as input, the storage systems described above may also support chatbots, talkbots, chatterbots, or artificial conversational entities or other applications that are configured to conduct a conversation via auditory or textual methods. Likewise, the storage system may actually execute such an application to enable a user such as a system administrator to interact with the storage system via speech. Such applications are generally capable of voice interaction, music playback, making to-do lists, setting alarms, streaming podcasts, playing audiobooks, and providing weather, traffic, and other real time information, such as news, although in embodiments in accordance with the present disclosure, such applications may be utilized as interfaces to various system management operations. 
     The storage systems described above may also implement AI platforms for delivering on the vision of self-driving storage. Such AI platforms may be configured to deliver global predictive intelligence by collecting and analyzing large amounts of storage system telemetry data points to enable effortless management, analytics and support. In fact, such storage systems may be capable of predicting both capacity and performance, as well as generating intelligent advice on workload deployment, interaction and optimization. Such AI platforms may be configured to scan all incoming storage system telemetry data against a library of issue fingerprints to predict and resolve incidents in real-time, before they impact customer environments, and captures hundreds of variables related to performance that are used to forecast performance load. 
     The storage systems described above may support the serialized or simultaneous execution of artificial intelligence applications, machine learning applications, data analytics applications, data transformations, and other tasks that collectively may form an AI ladder. Such an AI ladder may effectively be formed by combining such elements to form a complete data science pipeline, where exist dependencies between elements of the AI ladder. For example, AI may require that some form of machine learning has taken place, machine learning may require that some form of analytics has taken place, analytics may require that some form of data and information architecting has taken place, and so on. As such, each element may be viewed as a rung in an AI ladder that collectively can form a complete and sophisticated AI solution. 
     The storage systems described above may also, either alone or in combination with other computing environments, be used to deliver an AI everywhere experience where AI permeates wide and expansive aspects of business and life. For example, AI may play an important role in the delivery of deep learning solutions, deep reinforcement learning solutions, artificial general intelligence solutions, autonomous vehicles, cognitive computing solutions, commercial UAVs or drones, conversational user interfaces, enterprise taxonomies, ontology management solutions, machine learning solutions, smart dust, smart robots, smart workplaces, and many others. 
     The storage systems described above may also, either alone or in combination with other computing environments, be used to deliver a wide range of transparently immersive experiences (including those that use digital twins of various “things” such as people, places, processes, systems, and so on) where technology can introduce transparency between people, businesses, and things. Such transparently immersive experiences may be delivered as augmented reality technologies, connected homes, virtual reality technologies, brain—computer interfaces, human augmentation technologies, nanotube electronics, volumetric displays, 4D printing technologies, or others. 
     The storage systems described above may also, either alone or in combination with other computing environments, be used to support a wide variety of digital platforms. Such digital platforms can include, for example, 5G wireless systems and platforms, digital twin platforms, edge computing platforms, IoT platforms, quantum computing platforms, serverless PaaS, software-defined security, neuromorphic computing platforms, and so on. 
     The storage systems described above may also be part of a multi-cloud environment in which multiple cloud computing and storage services are deployed in a single heterogeneous architecture. In order to facilitate the operation of such a multi-cloud environment, DevOps tools may be deployed to enable orchestration across clouds. Likewise, continuous development and continuous integration tools may be deployed to standardize processes around continuous integration and delivery, new feature rollout and provisioning cloud workloads. By standardizing these processes, a multi-cloud strategy may be implemented that enables the utilization of the best provider for each workload. 
     The storage systems described above may be used as a part of a platform to enable the use of crypto-anchors that may be used to authenticate a product&#39;s origins and contents to ensure that it matches a blockchain record associated with the product. Similarly, as part of a suite of tools to secure data stored on the storage system, the storage systems described above may implement various encryption technologies and schemes, including lattice cryptography. Lattice cryptography can involve constructions of cryptographic primitives that involve lattices, either in the construction itself or in the security proof. Unlike public-key schemes such as the RSA, Diffie-Hellman or Elliptic-Curve cryptosystems, which are easily attacked by a quantum computer, some lattice-based constructions appear to be resistant to attack by both classical and quantum computers. 
     A quantum computer is a device that performs quantum computing. Quantum computing is computing using quantum-mechanical phenomena, such as superposition and entanglement. Quantum computers differ from traditional computers that are based on transistors, as such traditional computers require that data be encoded into binary digits (bits), each of which is always in one of two definite states (0 or 1). In contrast to traditional computers, quantum computers use quantum bits, which can be in superpositions of states. A quantum computer maintains a sequence of qubits, where a single qubit can represent a one, a zero, or any quantum superposition of those two qubit states. A pair of qubits can be in any quantum superposition of 4 states, and three qubits in any superposition of 8 states. A quantum computer with n qubits can generally be in an arbitrary superposition of up to 2{circumflex over ( )}n different states simultaneously, whereas a traditional computer can only be in one of these states at any one time. A quantum Turing machine is a theoretical model of such a computer. 
     The storage systems described above may also be paired with FPGA-accelerated servers as part of a larger AI or ML infrastructure. Such FPGA-accelerated servers may reside near (e.g., in the same data center) the storage systems described above or even incorporated into an appliance that includes one or more storage systems, one or more FPGA-accelerated servers, networking infrastructure that supports communications between the one or more storage systems and the one or more FPGA-accelerated servers, as well as other hardware and software components. Alternatively, FPGA-accelerated servers may reside within a cloud computing environment that may be used to perform compute-related tasks for AI and ML jobs. Any of the embodiments described above may be used to collectively serve as a FPGA-based AI or ML platform. Readers will appreciate that, in some embodiments of the FPGA-based AI or ML platform, the FPGAs that are contained within the FPGA-accelerated servers may be reconfigured for different types of ML models (e.g., LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that are contained within the FPGA-accelerated servers may enable the acceleration of a ML or AI application based on the most optimal numerical precision and memory model being used. Readers will appreciate that by treating the collection of FPGA-accelerated servers as a pool of FPGAs, any CPU in the data center may utilize the pool of FPGAs as a shared hardware microservice, rather than limiting a server to dedicated accelerators plugged into it. 
     The FPGA-accelerated servers and the GPU-accelerated servers described above may implement a model of computing where, rather than keeping a small amount of data in a CPU and running a long stream of instructions over it as occurred in more traditional computing models, the machine learning model and parameters are pinned into the high-bandwidth on-chip memory with lots of data streaming though the high-bandwidth on-chip memory. FPGAs may even be more efficient than GPUs for this computing model, as the FPGAs can be programmed with only the instructions needed to run this kind of computing model. 
     The storage systems described above may be configured to provide parallel storage, for example, through the use of a parallel file system such as BeeGFS. Such parallel files systems may include a distributed metadata architecture. For example, the parallel file system may include a plurality of metadata servers across which metadata is distributed, as well as components that include services for clients and storage servers. 
     The systems described above can support the execution of a wide array of software applications. Such software applications can be deployed in a variety of ways, including container-based deployment models. Containerized applications may be managed using a variety of tools. For example, containerized applications may be managed using Docker Swarm, Kubernetes, and others. Containerized applications may be used to facilitate a serverless, cloud native computing deployment and management model for software applications. In support of a serverless, cloud native computing deployment and management model for software applications, containers may be used as part of an event handling mechanisms (e.g., AWS Lambdas) such that various events cause a containerized application to be spun up to operate as an event handler. 
     The systems described above may be deployed in a variety of ways, including being deployed in ways that support fifth generation (‘5G’) networks. 5G networks may support substantially faster data communications than previous generations of mobile communications networks and, as a consequence may lead to the disaggregation of data and computing resources as modern massive data centers may become less prominent and may be replaced, for example, by more-local, micro data centers that are close to the mobile-network towers. The systems described above may be included in such local, micro data centers and may be part of or paired to multi-access edge computing (‘MEC’) systems. Such MEC systems may enable cloud computing capabilities and an IT service environment at the edge of the cellular network. By running applications and performing related processing tasks closer to the cellular customer, network congestion may be reduced and applications may perform better. 
     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 embodiments may be integrated with a computing device that may be specifically configured to perform one or more of the processes described herein. The computing device may include a communication interface, a processor, a storage device, and an input/output (“I/O”) module communicatively connected one to another via a communication infrastructure 
     A Communication interface may be configured to communicate with one or more computing devices. Examples of communication interface 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. 
     A Processor 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 may perform operations by executing computer-executable instructions (e.g., an application, software, code, and/or other executable data instance) stored in storage device. 
     A Storage device 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, the storage device 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. For example, data representative of computer-executable instructions configured to direct processor to perform any of the operations described herein may be stored within storage device. In some examples, data may be arranged in one or more databases residing within storage device. 
     An I/O module may include one or more I/O modules configured to receive user input and provide user output. The I/O module may include any hardware, firmware, software, or combination thereof supportive of input and output capabilities. For example, I/O module 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. 
     An I/O module 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 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. 
     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: 
     1. A method of [insert claim  1 ]. 
     2. The method of statement 1 wherein/further comprising [insert claim  2 ]. 
     3. The method of statement 2 or statement 1 wherein/further comprising [insert claim  3 ]. 
     4. The method of statement 3, statement 2, or statement 1 wherein/further comprising [insert claim  4 ]. 
     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. 4A  depicts effects of optimized and non-optimized read voltage levels on read values for an example single level cell flash memory. Threshold voltage  404  is on the horizontal axis of the graph, and the vertical axis represents the number of flash memory cells that have a particular threshold voltage. Erased or unprogrammed flash memory cells are depicted on one curve  406  that should have a read value of binary 1, and programmed or written flash memory cells are depicted on another curve  408  that should have a read value of binary 0. It should be appreciated that these curves  406 ,  408  (and further curves in  FIGS. 4B and 4C ) are not drawn to scale and are examples only, and that other read values and distributions of threshold voltage for erased or programmed cells, or values, ranges or polarities of threshold voltage, may be applicable. 
     Optimized read voltage level  402 , depicted as a vertical dashed line in  FIG. 4A , if or when applied as a read voltage to the flash memory cells in this example, results in the majority of erased or unprogrammed cells being read as the correct read value 1 and the majority of programmed cells being read as the correct read value 0. A small number of the erased or unprogrammed cells, on the lower right end of the curve  406 , will have a bit flip from 1 to 0, and be read in error as having a read value 0. Another small number of the programmed cells, on the lower left end of the curve  408 , will have a bit flip from 0 to 1, and be read in error as having a read value 1. Moving the read voltage level to the right in this example will increase the 0 to 1 bit flips and decrease the 1 to 0 bit flips. Moving the read voltage to the left in this example will increase the 1 to 0 bit flips and decrease the 0 to 1 bit flips. Locating the optimized read voltage level  402  at the intersection of the curves  406 ,  408  (as shown in  FIG. 4A ) results in an approximately equal number or quantity of 0 to 1 bit flips and 1 to 0 bit flips, and a bit flip ratio of bit flips 0 to 1 to bit flips 1 to 0 approximately equal to one. That is, 50% of the errors are 0 to 1 bit flips, divided by the other 50% of the errors, which are 1 to 0 bit flips, for a 50-50 ratio or 1. An inverse of the ratio  412  shown in  FIG. 4A  can also be used as the bit flip ratio  412 . In addition, a ratio of the quantity of 0 to 1 bit flips compared to the total number of errors, or the quantity of 1 to 0 bit flips compared to the total number of errors, could be used as a bit flip ratio  412  and compared to one half (or 0.5, i.e. 50%). This analysis presupposes an approximately equal distribution of ones and zeros throughout the data, and it should be appreciated that the embodiments can be adjusted with a bias for other mixes of ones and zeros. A threshold  410  for the comparison or the ratio  412  of bit flips can be devised to quantify the notion of “approximate” and guide decisions about adjusting read voltage level(s). 
       FIG. 4B  depicts optimized read voltage levels and read values for an example multilevel cell flash memory with two bits per cell. Analysis of  FIG. 4B  is an extension of analysis of  FIG. 4A . For two bits per cell MLC flash memory, there are four curves  416 ,  418 ,  420 ,  422  representing four levels of programming, from erased cell with read value binary 11 in curve  416 , through programmed levels of cell with read value binary 10 in curve  418 , and read value binary 00 in curve  420 , to read value binary 01 in curve  422 . It may be observed that the read values are a type or variation of Gray code (or reflected binary code, RBC) with only one bit changing per transition between any two adjacent read values. Other read value assignments are possible and readily devised. 
     Three optimized read levels  414  distinguish among the four programmed levels of the cell and four corresponding read values in  FIG. 4B . At the intersection of the curves  416 ,  418 , a first optimized read level  414 A is located where the bit flip ratio  412  (e.g., for the least significant bit of the read value in this example) is equal to 1 within a threshold  410 . At the intersection of the curves  418 ,  420 , a second optimized read level  414  B is located where the bit flip ratio  412  (e.g., for the most significant bit of the read value in this example) is equal to 1 within a threshold  410 . At the intersection of the curves  420 ,  422 , a third optimized read level  414  C is located where the bit flip ratio  412  (e.g., for the least significant bit of the read value in this example) is equal to 1 within a threshold  410 . In some embodiments of two bits per cell MLC flash memory, the most significant bit of the read value corresponds to a page type  424  known as lower page. The least significant bit of the read value corresponds to a page type  424  known as upper page. Other flash memories may have other assignments of page types  424 . 
       FIG. 4C  depicts optimized read voltage levels and read values for an example multilevel cell flash memory with three bits per cell, also known as triple level cell flash memory. Analysis of  FIG. 4C  is an extension of analysis of  FIGS. 4A and 4B . For three bits per cell MLC, i.e., TLC flash memory, there are eight curves  426 ,  428 ,  430 ,  432 ,  434 ,  436 ,  438 ,  440  representing eight levels of programming, from erased cell with read value binary 111 in curve  426 , through programmed levels of cell with read value binary 110 in curve  428 , read value binary 100 in curve  430 , read value binary 000 in curve  432 , read value binary 010 in curve  434 , read value binary 011 in curve  436 , and read value binary 001 in curve  438 , to read value 101 in curve  440 . 
     Seven optimized read levels  442  distinguish among the eight programmed levels of the cell and eight corresponding read values. At the intersection of the curves  426 ,  428 , a first optimized read level  442  A is located where the bit flip ratio  412  (e.g., for the least significant bit in this example) is equal to 1 within a threshold  410 . At the intersection of the curves  428 ,  430 , a second optimized read level  442  B is located where the bit flip ratio  412  (e.g., for the middle bit in this example) is equal to 1 within a threshold  410 . At the intersection of the curves  430 ,  432 , a third optimized read level  442  C is located where the bit flip ratio  412  (e.g., for the most significant bit in this example) is equal to 1 within a threshold  410 . At the intersection of the curves  432 ,  434 , a fourth optimized read level  442  D is located where the bit flip ratio  412  (e.g., for the middle bit in this example) is equal to 1 within a threshold  410 . At the intersection of the curves  434 ,  436 , a fifth optimized read level  442  E is located where the bit flip ratio  412  (e.g., for the least significant bit in this example) is equal to 1 within a threshold  410 . At the intersection of the curves  436 ,  438 , a sixth optimized read level  442  F is located where the bit flip ratio  412  (e.g., for the middle bit in this example) is equal to 1 within a threshold  410 . At the intersection of the curves  438 ,  440 , a seventh optimized read level  442  G is located where the bit flip ratio  412  (e.g., for the most significant bit in this example) is equal to 1 within a threshold  410 . In some embodiments of three bits per cell MLC or TLC flash memory, the most significant bit of the read value corresponds to a page type  424  known as lower page. The middle bit corresponds to a page type  424  known as upper page. And, the least significant bit corresponds to a page type  424  known as extra page. The above analysis is readily extended to quad level cell (QLC) flash memory, also known as four bits per cell MLC, and higher numbers of bits per cell MLC flash memory. Various bit assignments, page assignments and read values, etc., may apply to the embodiments described herein. 
       FIG. 5  depicts an example command for adjusting a read voltage level of a multilevel cell flash memory. To set or adjust a specific read voltage level, a command  502  with address  504  and data  506  is sent to the flash memory device. The command  502  indicates to set a read voltage level, the address  504  indicates which level is to be set, and the data  506  indicates an offset value for the selected read voltage level. The offset value could be an offset from a default read voltage level, an offset from a lowest read voltage level or an offset from the highest read voltage level, a relative value, a signed value or an absolute value, etc. Other command sequences or communication mechanisms for adjusting or setting a read voltage level may be devised for other flash memory devices. 
       FIG. 6  sets forth a system diagram of a storage system that tracks bit flips and adjusts read voltage levels of multilevel cell flash memory. Mechanisms described herein are readily applied to various storage systems, including storage arrays and storage clusters with storage nodes as described herein, for further embodiments. Flash memory  608  has multiple packages  612 , each of which may have multiple LUNs  614  (logical unit numbers, which may be individual integrated circuits or ICs). One or more processors  602  perform writes to and reads from the flash memory  608 , applying error correction  604 . Based on error correction, the processor(s)  602  can determine when a bit flip has occurred, i.e., when the true or corrected value of a bit differs from the read value of the bit in the original, raw data read from the flash memory prior to error correction. A memory  606  (e.g., RAM, DRAM, NVRAM, or flash memory in some embodiments) has a data structure  610 , which the processor(s)  602  use for tracking bit flips in reads of flash memory  608 , and tracking offsets or other adjustments or settings of read voltage level(s) for the flash memory  608 , in various embodiments. 
     In one embodiment, the storage system determines and tracks a bit flip ratio (or comparison) in reads of flash memory  608  on a per block  616  of flash memory basis, storing bit flip ratio associated with block ID in the data structure  610  in memory  606 . When the bit flip ratio for a specific block  616  of flash memory is less than or greater than one by more than a threshold  410 , or comparison shows the bit flip ratios not equal to within a threshold, this triggers the processor(s)  602  to determine one or more new settings for read voltage level(s) of the flash memory  608 , and record one or more new offsets in the data structure  610  associated with a block ID. In turn, the new offsets in the data structure  610  trigger the adjustment of the read voltage levels, for the read of data in the specified block. 
     In some embodiments, bit flip ratio (or comparison) is tracked relative to a specific page type  424 , and the processor(s)  602  determine a specific optimized read level that corresponds to bit flips that are relative to that specific page type  424 . For example, the system could determine bit flips relative to just the lower page of two bit, three bit or more bits MLC flash memory, and track the corresponding bit flip ratio, then determine the optimized read voltage level that is causative to bit flips relative to the lower page. From this one optimized read voltage level, the processor(s)  602  then extrapolate the other optimized read voltage levels that correspond to bit flips relative to other pages. Similar embodiments track bit flips relative to middle page, or upper page, etc., and perform similar optimization of a read voltage level and extrapolation to other optimized read voltage levels. Extrapolation can be done by calculating read voltage levels based on lab work or testing, or theoretical analysis of transistors and circuitry, etc. In further embodiments, bit flip ratio  412  (or comparison) is tracked relative to all page types  424  of whichever flash memory  608  is used in the storage system. The processor(s)  602  determine optimized read levels corresponding to bit flips that are relative to each of the page types  424 . 
     In some embodiments, the storage system tracks bit flip ratios or comparisons and offsets or settings for read voltage levels on a per block  616  basis, and directs the setting or adjustment of read voltage levels in the flash memory on a per LUN  614  basis. For example, the storage system determines that an upcoming page read is for a page  618  in a block  616  that should have one set of read voltage levels, but the LUN  614  holding the block  616  and page  618  is presently at another adjustment level of read voltage levels, for a previous read of some other page  618  in some other block  616 . Next, the storage system commands the LUN  614  to adjust to the read voltage levels determined for the block  616  that has the page  618  for the desired read. After read voltage level adjustment, the storage system reads the page  618  from flash memory  608 , which uses the new read voltage levels. The storage system iterates revising of the read voltage levels and does not need to restore the flash memory to default read levels. Each read, of a page  618  in a specific block  616 , can use optimized read voltage levels specific to that block  616 , as managed by the processor(s)  602  with the data structure  610 , even though the flash memory  608  itself only adjusts read voltage levels on a per LUN  614  basis, as commanded. Because the storage system iteratively tunes read voltage levels on an ongoing basis, data reads are optimized for minimized errors. In some embodiments, all reads require error correction. Adjusting read voltage maximizes probability of error correction on the first try. In further embodiments, most reads will be successful without error correction, and the data reads that need error correction are unlikely to need retries. 
       FIG. 7  is a flow diagram of a method of biasing read voltage for flash memory in a storage system, which is practiced by the storage system and more specifically by one or more processors of the storage system. The storage system could be a storage array with centralized storage processor or processors, or a storage cluster with distributed storage nodes and distributed processing, among further embodiments. In an action  702 , the storage system tracks bit flips, per block of flash memory, relative to upper page, lower page or extra page. Bit flips are determined based on error correction of data reads in some embodiments. The tracking could be specific to one of these page types, or to two or more, or all of the page types in various embodiments. In an action  704 , the storage system compares or forms a ratio of 0 to 1 bit flips and 1 to 0 bit flips. As part of the tracking, this is done on a per block basis and/or specific to one page type, or two or more or all page types. 
     In a decision action  706 , the storage system determines whether the comparison or ratio exceeds a threshold. In some embodiments, the threshold includes a lower threshold and an upper threshold, in order to keep the ratio in a close range, for example between 0.5 and 1, depending on implementation. This prevents the algorithm from being “greedy”. If the answer is no, the threshold has not been exceeded, flow branches back to the action  702 , to continue tracking bit flips. If the answer is yes, the comparison or the ratio exceeds the threshold, flow proceeds to the action  708 . In the action  708 , the storage system determines the offset for a read voltage level, based on the comparison or the ratio. In an action  710 , the storage system extrapolates other read voltage levels or offsets. Extrapolation could be based on theoretical calculations, laboratory measurements or characterization of flash memories. In an action  712 , the storage system forms one or more commands to adjust one or more read voltage levels. In an action  714 , the storage system sends one or more commands to adjust one or more read voltage levels, to the flash memory. The storage system could use the example command shown in  FIG. 5 , or other command or mechanism, as appropriate to a specific flash memory. 
     In some versions, the adjustments are made only if overall number of bit flips as above a specified threshold. If the number of errors is approaching the error correction capability and errors are skewed, the tracking engages to optimize the read threshold and reduce the number of errors coming from the media. 
     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. 8  is an illustration showing an exemplary computing device which may implement the embodiments described herein. The computing device of  FIG. 8  may be used to perform embodiments of the functionality for tracking of optimum read voltage thresholds in flash memory in accordance with some embodiments. The computing device includes a central processing unit (CPU)  801 , which is coupled through a bus  805  to a memory  803 , and mass storage device  807 . Mass storage device  807  represents a persistent data storage device such as a disc drive, which may be local or remote in some embodiments. The mass storage device  807  could implement a backup storage, in some embodiments. Memory  803  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  803  or mass storage device  807  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  801  may be embodied in a general-purpose processor, a special purpose processor, or a specially programmed logic device in some embodiments. 
     Display  811  is in communication with CPU  801 , memory  803 , and mass storage device  807 , through bus  805 . Display  811  is configured to display any visualization tools or reports associated with the system described herein. Input/output device  809  is coupled to bus  805  in order to communicate information in command selections to CPU  801 . It should be appreciated that data to and from external devices may be communicated through the input/output device  809 . CPU  801  can be defined to execute the functionality described herein to enable the functionality described with reference to  FIGS. 1-7 . The code embodying this functionality may be stored within memory  803  or mass storage device  807  for execution by a processor such as CPU  801  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 also be integrated with a virtualized computing system implemented with physical computing resources. 
       FIG. 9  is a system action diagram depicting a storage system  901  iterating adjustment of an optimal read voltage level for flash memory  904 , in accordance with an embodiment of the description. The storage system  901  is minimally depicted with one or more processors  902  and flash memory  904 , but could have further components as readily devised for storage systems. The processor(s)  902  iterate  906  a process of reads of the flash memory  904 , determination of a gradient and adjustment of the optimum read voltage level. In some versions, the determination of a gradient and adjustment of read voltage level is performed continually through regular operation of the storage system  901 , during user accesses to user data, i.e., I/O operations. Testing for a gradient, and possible adjustment of optimum read voltage level could happen at regular or irregular time intervals, after regular or irregular numbers of data accesses, or in response to numbers of errors or error rates, in various embodiments. In other versions, the adjustment of optimum read voltage level happens at one or more specific calibration interval(s) outside of user accesses to user data, for example on power up or in response to the number of errors or error rate meeting a threshold. Graphs  908 ,  910 ,  912 ,  914  illustrate various cases for the gradient determination and adjustment of optimum read voltage level, as further described below. 
     In operation, the storage system  901  performs multiple actions to iterate  906  the process of adjusting optimum read voltage level. In the first action  916 , identified by a circled number  1  in the graphs  908 ,  910 ,  912 ,  914 , the storage system  901  performs a first read of data at an address, at an optimum read voltage level. This optimum read voltage level is previously determined, for example in system startup and later in iterations of this process of adjusting optimum read voltage level. The storage system  901  determines the number of bit errors in the data read at the optimum read voltage level. In the second action  918 , identified by a circled number  2  in the graphs  908 ,  910 ,  912 ,  914 , the storage system  901  performs a second read of the data at that same address, at another read voltage level, which could be termed a test read voltage level (since this new read voltage level is being tested for errors). The storage system  901  determines the number of bit errors in the data read at the test read voltage level. 
     In the third action  920 , identified by a circled number  3  in the graphs  908 ,  910 ,  912 ,  914 , the storage system  901  determines whether there is a gradient between the number of bit errors in the data read at the optimum read voltage level (circled  1 ) versus the number of bit errors in the data read at the test read voltage level (circled  2 ). Gradient here may refer to a nonzero slope, or more specifically more errors or fewer errors at one read voltage level than at the other read voltage level. A nonzero gradient means that the previously determined optimum read voltage level is not at the theoretical optimum read voltage level, where movement (or change) of read voltage level to either side will produce a relatively small gradient or zero gradient in some embodiments. Under such circumstances, with a nonzero gradient, it is advantageous to adjust the optimum read voltage level in a direction towards the theoretical optimum read voltage level, which is practically determined by observing the gradient, as illustrated in the graphs  908 ,  910 ,  912 ,  914  and further explained below. If there is a (nonzero) gradient, the storage system  901 , more specifically the processor(s)  902 , adjusts the optimum read voltage level. This is accomplished, for example by setting a parameter for read voltage level and communicating to the flash memory  904 . The next read of data, at whichever address is to be read next, occurs at the newly adjusted optimum read voltage level. After that, the system continues iterating  906 , to continually or discreetly adjust the optimum read voltage level. 
     All of the graphs  908 ,  910 ,  912 ,  914  in  FIG. 9  have the read voltage level on the horizontal axis and the number of bit errors in a data read on the vertical axis, with the theoretical optimum read voltage level depicted as a dashed vertical line. These graphs show that reading at the theoretical optimum read voltage level shows the lowest number of errors, and reading at a lesser or greater read voltage level results in a larger number of errors. In various embodiments, the storage system  901  could operate under the conditions shown in the graphs  908 ,  910  for Case A and Case B, where the second read is at an incrementally greater read voltage level than the first read (at the previously determined optimum read voltage level). Alternatively the storage system  901  could operate under the conditions shown in the graphs  912 ,  914  for Case C and Case D, where the second read is at an incrementally lower read voltage level than the first read (at the previously determined optimum read voltage level). Some embodiments could operate under all four conditions or cases, for example by alternating second reads at higher and lower voltages than the previously determined optimum read voltage level used during the first read. In one embodiment, the choice of whether the second read is higher or lower is based on the slope of the previously determined gradient. For example, a positive error gradient from a previously determined optimum read voltage level to a higher test read voltage level could result in the next test read voltage level being lower than the next determined optimum read voltage, and vice versa. Other conditions are readily developed in keeping with the teachings herein. 
     Case A, illustrated in the topmost graph  908 , has the storage system  901  reading data at an optimum read voltage level that was previously determined (circled  1 ), but, unbeknownst to the storage system just yet, is to the right or at a higher read voltage level than the theoretical optimum read voltage level. The storage system determines a number of errors from this read, for example by tracking bit errors or bit error rate. Next, the storage system  901  reads the data, at the same address, at a second or test read voltage level (circled  2 ). In this case, the second or test read voltage level is higher or to the right of the read voltage level used in the first read, for example by a specified increment. The storage system determines the number of errors from this second read, and determines that this number of errors is greater than the number of errors observed in the first data read. Based on this comparison, the storage system adjusts the optimum read voltage level in a direction away from the test read voltage (circled  3 ), in this case to a lower read voltage level, for example by an increment. The adjustment increment can be less than, equal to, or greater than the increment between the test read voltage level and the previously determined optimum read voltage level, in various embodiments. 
     Case B, illustrated in the next lower graph  910  from the top, has the storage system  901  reading data at an optimum read voltage level that was previously determined (circled  1 ), but, unbeknownst to the storage system just yet, is to the left or at a lower read voltage level than the theoretical optimum read voltage level. The storage system determines a number of errors from this read, for example by tracking bit errors or bit error rate. Next, the storage system  901  reads the data, at the same address, at a second or test read voltage level (circled  2 ). In this case, the second or test read voltage level is higher or to the right of the read voltage level used in the first read, for example by a specified increment. The storage system determines the number of errors from this second read, and determines that this number of errors is less than the number of errors observed in the first data read. Based on this comparison, the storage system adjusts the optimum read voltage level in a direction towards the test read voltage (circled  3 ), in this case to a higher read voltage level, for example by an increment. The adjustment increment can be less than, equal to, or greater than the increment between the test read voltage level and the previously determined optimum read voltage level, in various embodiments. In some embodiments, the system adjusts the optimum read voltage level to the test read voltage level. That is, the adjustment increment is the same as the increment between the test read voltage level and the previously determined optimum read voltage level. 
     Case C, illustrated in the next lower graph  912 , i.e., the second graph from the bottom, has the storage system  901  reading data at an optimum read voltage level that was previously determined (circled  1 ), but, unbeknownst to the storage system just yet, is to the right or at a higher read voltage level than the theoretical optimum read voltage level. The storage system determines a number of errors from this read, for example by tracking bit errors or bit error rate. Next, the storage system  901  reads the data, at the same address, at a second or test read voltage level (circled  2 ). In this case, the second or test read voltage level is lower or to the left of the read voltage level used in the first read, for example by a specified increment. The storage system determines the number of errors from this second read, and determines that this number of errors is less than the number of errors observed in the first data read. Based on this comparison, the storage system adjusts the optimum read voltage level in a direction away from the test read voltage (circled  3 ), in this case to a lower read voltage level, for example by an increment. The adjustment increment can be less than, equal to, or greater than the increment between the test read voltage level and the previously determined optimum read voltage level, in various embodiments. In some embodiments, the system adjusts the optimum read voltage level to the test read voltage level. 
     Case D, illustrated in the lowermost graph  914 , has the storage system  901  reading data at an optimum read voltage level that was previously determined (circled  1 ), but, unbeknownst to the storage system just yet, is to the left or at a lower read voltage level than the theoretical optimum read voltage level. The storage system determines a number of errors from this read, for example by tracking bit errors or bit error rate. Next, the storage system  901  reads the data, at the same address, at a second or test read voltage level (circled  2 ). In this case, the second or test read voltage level is lower or to the left of the read voltage level used in the first read, for example by a specified increment. The storage system determines the number of errors from this second read, and determines that this number of errors is greater than the number of errors observed in the first data read. Based on this comparison, the storage system adjusts the optimum read voltage level in a direction away from the test read voltage (circled  3 ), in this case to a higher read voltage level, for example by an increment. The adjustment increment can be less than, equal to, or greater than the increment between the test read voltage level and the previously determined optimum read voltage level, in various embodiments. 
       FIG. 10  is a flow diagram of a further method of biasing read voltage for flash memory in a storage system, which is practiced by the storage system and more specifically by one or more processors of the storage system. The flash memory could be single level cell, and the read voltage level is the only read voltage level used for reading data. Alternatively, the flash memory could be multilevel cell (e.g., TLC or triple level cell, QLC or quad level cell, or greater number of levels), and the read voltage level being adjusted is one of the read voltage levels (e.g., seven read voltage levels for TLC, fifteen read voltage levels for QLC, etc.) In an action  1002 , bit errors are determined for a read of data at an address in flash memory, at the optimum read voltage level. This optimum read voltage level is previously determined, i.e., in a previous iteration. As an example, refer to the circled  1  in the graphs in  FIG. 9 . It should be appreciated that on startup where the optimum threshold value may not be established, an initial threshold value that can be used to correct the data and obtain the bit flip count may be utilized in some embodiments. 
     In an action  1004 , bit errors are determined for a read of the data at the same address, using a test read voltage level. The test read voltage could be greater than the previously determined optimum read voltage level, or less than the previously determined optimum read voltage level, by an increment. As an example, refer to the circled  2  in the graphs in  FIG. 9 . In a determination action  1006 , it is determined whether there is a gradient between bit errors at the optimum read voltage level and the bit errors at the test read voltage level. For example, the number of bit errors at the test read voltage level in the second read, in the action  1004 , could be greater than, or less than, the number of bit errors at the optimum read voltage level used in the first read in the action  1002 . If there is no gradient, i.e., the gradient is zero because the number of bit errors at the two read voltage levels is the same, there is no adjustment to optimum read voltage level, and flow proceeds to the action  1010  to prepare for the next data read in some embodiments. If there is a gradient, i.e., the gradient is nonzero because the number of bit errors differs at the two read voltage levels, flow proceeds to the action  1008 . 
     In the action  1008 , the optimum read voltage level is adjusted. If the number of bit errors for the read at the test read voltage level is greater, the optimum read voltage level is adjusted in a direction away from the test read voltage level. If the number of bit errors for the read at the test read voltage level is lower, the optimum read voltage level is adjusted in a direction towards the test read voltage level. More specifically, if the number of bit errors is greater for the second read and the second read is at a read voltage level that is greater than the optimum read voltage level used for the first read (e.g., refer to  FIG. 9  Case A), the optimum read voltage level as adjusted downward or lower, to a lower voltage level. If the number of bit errors is lower for the second read and the second read is at a read voltage level that is greater than the optimum read voltage level used for the first read (e.g., see  FIG. 9  Case B), the optimum read voltage level is adjusted upward or higher, to a higher voltage level. If the number of bit errors is lower for the second read and the second read is at a read voltage level that is lower than the optimum read voltage level used for the first read (e.g., see  FIG. 9  Case C), the optimum read voltage level is adjusted downward or lower, to a lower voltage level. If the number of bit errors is greater for the second read and the second read is at a read voltage level that is lower than the optimum read voltage used for the first read (e.g., see  FIG. 9  Case D), the optimum read voltage level is adjusted upward or higher, to a higher voltage level. In the action  1010 , the next address for a data read is determined. Flow then proceeds back to the action  1002 , to iterate the process of determining bit errors for two different read voltage levels, determining whether a gradient is present, and adjusting the optimum read voltage level. 
     With reference to  FIGS. 9 and 10 , further aspects of some embodiments of the storage system are discussed below. In Cases A and D, depicted in graphs  908 ,  914  in  FIG. 9 , there is no test for the next optimum read voltage level to which the optimum read voltage level is adjusted after the determination of a gradient. That is, there is no read of the same data using the next value of the optimum read voltage level prior to changing to the next optimum read voltage level and proceeding directly to reading user data that the next address to be read. This is contrasted with Cases B and C, depicted in graphs  910 ,  912  in  FIG. 9 . In embodiments where the adjustment increment is the same as the increment between the optimum read voltage level and the test read voltage level, Cases B and C in a sense test the next value of the optimum read voltage level in the second read (circled  2 ), prior to changing to the next optimum read voltage level and proceeding to reading user data using the new optimum read voltage level. Such is not the case with Cases A and D, were there is no testing for bit errors for a test read with the to-be-adjusted-to optimum read voltage prior to actually adjusting the optimum read voltage and reading data in a regular access to user data. Further embodiments are readily adapted for multilevel cell flash memory. Each of multiple read voltage levels could be tracked and adjusted, independently of the other read voltage levels. Adjustment increments could be always by the same increment, up or down, in some embodiments. Alternatively, the adjustment increment could be a variable, or adjustable between single and multiples of a specified increment, in further embodiments. 
       FIG. 11  depicts a flash memory device architecture of word lines  1102  and bit lines  1104 . A flash memory array has floating gate transistors  1112  organized in rows and columns, with each string  1118  of series or NAND-connected floating gate transistors  1112  connected to a corresponding bit line  1104  in a column, and word lines  1102  in rows driving gates of the floating gate transistors  1112 . Each string  1118  has a ground select transistor  1116  connected to a source line  1110  and a string select transistor  1114  connected to the bit line  1104 , with the series or NAND-connected floating gate transistors  1112  between the ground select transistor  1116  and the string select transistor  1114 . Multiple strings  1118  are NOR-connected to each bit line  1104  through the corresponding string select transistors  1114 . To read a selected floating gate transistor  1112 , a word line  1102  connected to the external gate of the floating gate transistor  1112  is activated at an appropriate read voltage threshold, and all other word lines  1102  connected to the other floating gate transistors  1112  in the string  1118  are raised to a higher voltage so as to activate those transistors regardless of programming level. The ground select line  1108  of the string  1118  and the string select line  1106  are activated, and the string  1118  discharges the corresponding bit line  1104 , or not, depending on whether the selected floating gate transistor  1112  is erased or programmed. The above structure and process are readily understood in terms of single level cell memory and also multilevel cell memory of two, three, four or more bits per cell. 
       FIG. 12  depicts a flash memory device architecture of pages  1210  within blocks  1208 , selected through a block decoder  1202  and page decoder  1204 , with data read-out through a page buffer  1214 . Physical address decoding through the block decoder  1202  and page decoder  1204  activates the string select line  1106 , a selected word line  1102  and other word lines  1102  in an array  1206  as described above with reference to  FIG. 11 , and the ground select line  1108 , for multiple strings  1118  to read-out corresponding bit lines  1104  of a selected page  1210  in a selected block  1208 , through the page buffer  1214 . For system embodiments described below with reference to  FIG. 13 , it should be appreciated that various flash memory device architectures for various specific flash memory devices and manufacturers vary as to routing of word lines  1102  in the blocks  1208  and pages  1210 . For example, some flash memory devices have each word line  1102  connected to multiple pages  1210  in a block  1208 . In other instances some flash memory devices have each word line  1102  connected to pages  1210  in multiple blocks  1208 . Connections of word lines to pages  1210  and blocks  1208  may vary with memory size, number of layers in device manufacture, and further design considerations. 
       FIG. 13  depicts a system that optimizes read voltage levels for accesses to pages  1210  or blocks to which a word line  1102  connects. The system is aware of various flash memory architectures, and optimizes accordingly as explained through the example embodiments contained herein. A processing device  1302 , for example one or more processors, performs a read voltage level optimization process  1310  and a read voltage level adjust for accesses process  1312 , for accessing flash memory  1308 . For use in these processes  1310 ,  1312 , a flash architecture library  1304  is available, and the system maintains a data structure that associates sets  1314  of pages or blocks with sets  1316  of optimum read voltage levels for corresponding pages or blocks in a memory  1306 . This data or metadata in memory  1306  could be in various forms, including lists, a database or unstructured data. The association of the sets  1314 ,  1316  could be represented by a word line identifier, a set identifier, or a page or block identifier, etc. 
     In one embodiment, the read voltage level optimization process  1310  determines optimized read voltage levels using mechanisms described above with reference to  FIGS. 4A-10 . The system is aware of flash memory architectures, through use of the flash architecture library  1304 , which could include manufacturer-supplied, third-party, or other information about word line connections to pages  1210  and blocks  1208  in specific flash memory devices. Through user input, or alternatively through polling of the flash memory  1308 , the system determines which flash memory devices are present in flash memory  1308 , and defines sets of pages or blocks to which word lines  1102  connect, in accordance with the appropriate flash memory architecture, e.g., as accessed in the flash architecture library  1304 . 
     It would be burdensome for a storage system to determine and store optimum read voltage levels for each and every page  1210  of flash memory  1308 . To solve this technological problem, embodiments of a storage system determine read voltage level(s) (e.g., one for SLC, multiples for MLC) for a page or pages of a flash memory device that are accessed by activating a specific word line, and extend the use of the optimal read voltage level(s) to access further pages in a set of pages or blocks of the flash memory device to which the same word line connects. The system stores sets  1314  of pages or blocks in association with sets  1316  of optimum read voltage levels in memory  1306 , for word lines, as described above. 
     In one embodiment, the read voltage level adjustment for accesses process  1312  determines whether the read voltage level is already set at an optimized read voltage level for pages or blocks to which a word line connects, when a new access is initiated to an address in the flash memory  1308 . For example, the system could use address translation to determine the physical address in a flash memory device, and test or determine whether the page that is about to be accessed belongs in a set of pages or blocks to which a specific word line connects and for which the read voltage level(s) of that flash memory device have been set at the corresponding optimal read voltage level(s). If so, the access proceeds, using the previously set optimum read voltage level(s) to access the page. If not, the system sends the optimized read voltage level(s) for accessing pages or blocks to which a specific word line  1106  connects to the flash memory device  1206 , and then proceeds to access the page, which belongs (i.e., is included) in that set of pages or blocks to which the specific word line  1106  connects. In some embodiments, where a previously set optimum read voltage is not available, the system may determine an optimum read voltage value through the embodiments described above with reference to  FIGS. 4-10 . It should be appreciated that the embodiments described above may be integrated into systems having heterogeneous types of flash memory within the system, e.g., a storage node, storage unit, and/or a blade of the system. In addition, the embodiments may be integrated into a distributed system as described above. 
       FIG. 14  is a flowchart diagram illustrating method operations for managing read voltage threshold levels in a flash memory device in accordance with some embodiments, which is practiced by the storage system and more specifically by one or more processors of the storage system. The storage system could be a storage array with centralized storage processor or processors, or a storage cluster with distributed storage nodes and distributed processing, among further embodiments. 
     The method initiates with operation  1402  where a determination to which set of pages or blocks of a flash memory device a word line connects, according to a flash memory device architecture, for each of a plurality of word lines is performed. In some embodiments, the flash memory architecture also includes the number of bits per cell for the flash memory device. Here, the determination or identification may be made through a library or some other table or data structure accessible to the system as discussed above and in particular with reference to  FIG. 13 . The method then advances to operation  1404  where one or more optimum read voltage levels is determined or identified for one or more pages of the flash memory device belonging to the determined set of pages or blocks of the flash memory device to which the word line connects. It should be appreciated that this determination or identification may be made for each of the plurality of word lines. 
     Continuing with  FIG. 14 , the method advances to operation  1406  where the determined optimum read voltage levels in association with the sets of pages or blocks of the flash memory device to which the word lines connect is maintained or stored in memory associated with the system, for each of the plurality of word lines. As discussed above, the optimum read voltage levels may be stored in a table or other data structure in some embodiments. In operation  1408 , the method utilizes the optimum read voltage levels, as maintained in memory, to access further pages in the flash memory device. It should be appreciated that each such access of a page uses the one or more determined optimum read voltage levels associated with the word line that connects to the set of pages or blocks that includes the page being accessed as described above with respect to  FIGS. 11-13 .