METHODS TO IDENTIFY, HANDLE AND RECOVER FROM SUSPECT SSDS IN A CLUSTERED FLASH ARRAY

Abstract

A technique predicts failure of one or more storage devices of a storage array serviced by a storage system and for establishes one or more threshold conditions for replacing the storage devices. The predictive technique periodically monitors soft and hard failures of the storage devices (e.g., from Self-Monitoring, Analysis and Reporting Technology), as well as various usage counters pertaining to input/output (I/O) workloads and response times of the storage devices. A heuristic procedure may be performed that combines the monitored results to calculate the predicted failure and recommend replacement of the storage devices, using one or more thresholds based on current usage and failure patterns of the storage devices. In addition, one or more policies may be provided for replacing the storage devices in a cost-effective manner that ensures non-disruptive operation and/or replacement of the SSDs, while obviating a potential catastrophic scenario based on the usage and failure patterns of the storage devices.

Description

RELATED APPLICATION

The present application is a continuation of U.S. patent application Ser. No. 14/673,258, entitled METHODS TO IDENTIFY, HANDLE AND RECOVER FROM SUSPECT SSDS INA CLUSTERED FLASH ARRAY, filed on Mar. 30, 2015 by Anish Gupta et al., the contents of which are incorporated by reference herein in their entirety.

BACKGROUND

Technical Field

The present disclosure relates to storage systems and, more specifically, to predictive replacement of storage devices of a storage system.

Background Information

A storage system typically includes a storage array having one or more storage devices into which information may be entered, and from which the information may be obtained, as desired. The storage devices may include hard disk drives (HDDs) embodied as magnetic disk devices having mechanically wearing components (e.g., spindles and moving magnetic heads) and solid state drives (SSDs) embodied as flash storage devices having electronically wearing components. For example, some types of SSDs, especially those with NAND flash components, may be configured with erasable pages or segments, each of which may have a limited endurance, i.e., a limited number of erase cycles, before being unable to store data reliably. Wear-leveling may be employed to address this limitation by arranging the information so that erasure and rewrite operations are distributed evenly across components of the devices. In addition, usage patterns pertaining to input/output (I/O) workloads serviced by the storage system may target substantially all of the SSDs of the array so as to further distribute the information evenly among the drives.

However, such even distribution of operations and workloads may cause wear-out of the SSDs to occur at approximately the same time, thereby leading to a potentially catastrophic failure scenario, i.e., deterioration of redundancy and loss of data. Such even wear-out is unusual for HDDs, which typically have one or two disk failures occur at a time. In addition, HDD-based errors (e.g., typically based on mechanical failures) may be reported differently and have different error pattern characteristics than SSD-based errors. As such, a predictive failure technique to detect and replace storage media likely to fail as a group at approximately the same time, is needed.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and further advantages of the embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:

FIG. 1 is a block diagram of a plurality of nodes interconnected as a cluster;

FIG. 2 is a block diagram of a node;

FIG. 3 is a block diagram of a storage input/output (I/O) stack of the node;

FIG. 4 illustrates a write path of the storage I/O stack;

FIG. 5 illustrates a read path of the storage I/O stack;

FIG. 6 illustrates segment cleaning by a layered file system of the storage I/O stack;

FIG. 7a illustrates a RAID stripe formed by the layered file system;

FIG. 7b illustrates changes to a segment-based RAID configuration in accordance with changes to a storage pool of the cluster;

FIG. 8 illustrates a RAID configuration topology tree structure; and

FIG. 9 is a flow diagram of a technique for predicting failure of one or more storage devices serviced by the node of the cluster.

OVERVIEW

The embodiments described herein provide a technique for predicting failure of one or more storage devices, such as solid state drives (SSDs), of a storage array serviced by a storage system (node) and for establishing one or more threshold conditions for replacing the SSDs of the storage array. To that end, the predictive technique periodically monitors soft and hard failures of the SSDs (e.g., from Self-Monitoring, Analysis and Reporting Technology), as well as various usage counters pertaining to input/output (I/O) workloads and response times of the SSDs. A heuristic procedure may then be performed that combines the monitored results to calculate the predicted failure and recommend replacement of the SSDs using one or more thresholds based on current usage and failure patterns of the SSDs. In addition, one or more policies may be provided for replacing the SSDs of the storage array in a cost-effective manner that ensures non-disruptive operation and/or replacement of the SSDs, while obviating a potential catastrophic scenario based on the usage and failure patterns of the SSDs, e.g., deterioration of redundancy resulting in data loss. As for the latter, the predictive technique may provide a “warning” to enable replacement of the SSDs prior to such catastrophic failure while maintaining an expected level of reliability for data stored in the storage array. That is, an alert indicating replacement of SSDs is provided prior to a catastrophic failure such that an expected level of redundancy is maintained for the storage array.

DESCRIPTION

Storage Cluster

FIG. 1 is a block diagram of a plurality of nodes 200 interconnected as a cluster 100 and configured to provide storage service relating to the organization of information on storage devices. The nodes 200 may be interconnected by a cluster interconnect fabric 110 and include functional components that cooperate to provide a distributed storage architecture of the cluster 100, which may be deployed in a storage area network (SAN). As described herein, the components of each node 200 include hardware and software functionality that enable the node to connect to one or more hosts 120 over a computer network 130, as well as to one or more storage arrays 150 of storage devices over a storage interconnect 140, to thereby render the storage service in accordance with the distributed storage architecture.

Each host 120 may be embodied as a general-purpose computer configured to interact with any node 200 in accordance with a client/server model of information delivery. That is, the client (host) may request the services of the node, and the node may return the results of the services requested by the host, by exchanging packets over the network 130. The host may issue packets including file-based access protocols, such as the Network File System (NFS) protocol over the Transmission Control Protocol/Internet Protocol (TCP/IP), when accessing information on the node in the form of storage containers such as files and directories. However, in an embodiment, the host 120 illustratively issues packets including block-based access protocols, such as the Small Computer Systems Interface (SCSI) protocol encapsulated over TCP (iSCSI) and SCSI encapsulated over FC (FCP), when accessing information in the form of storage containers such as logical units (LUNs). Notably, any of the nodes 200 may service a request directed to a storage container stored on the cluster 100.

FIG. 2 is a block diagram of a node 200 that is illustratively embodied as a storage system having one or more central processing units (CPUs) 210 coupled to a memory 220 via a memory bus 215. The CPU 210 is also coupled to a network adapter 230, storage controllers 240, a cluster interconnect interface 250, and a non-volatile random access memory (NVRAM 280) via a system interconnect 270. The network adapter 230 may include one or more ports adapted to couple the node 200 to the host(s) 120 over computer network 130, which may include point-to-point links, wide area networks, virtual private networks implemented over a public network (Internet) or a local area network. The network adapter 230 thus includes the mechanical, electrical and signaling circuitry needed to connect the node to the network 130, which illustratively embodies an Ethernet or Fibre Channel (FC) network.

The memory 220 may include memory locations that are addressable by the CPU 210 for storing software programs and data structures associated with the embodiments described herein. The CPU 210 may, in turn, include processing elements and/or logic circuitry configured to execute the software programs, such as a storage input/output (I/O) stack 300, and manipulate the data structures. Illustratively, the storage I/O stack 300 may be implemented as a set of user mode processes that may be decomposed into a plurality of threads. An operating system kernel 224, portions of which are typically resident in memory 220 (in-core) and executed by the processing elements (i.e., CPU 210), functionally organizes the node by, inter alia, invoking operations in support of the storage service implemented by the node and, in particular, the storage I/O stack 300. A suitable operating system kernel 224 may include a general-purpose operating system, such as the UNIX® series or Microsoft Windows® series of operating systems, or an operating system with configurable functionality such as microkernels and embedded kernels. However, in an embodiment described herein, the operating system kernel is illustratively the Linux® operating system. It will be apparent to those skilled in the art that other processing and memory means, including various computer readable media, may be used to store and execute program instructions pertaining to the embodiments herein.

Each storage controller 240 cooperates with the storage I/O stack 300 executing on the node 200 to access information requested by the host 120. The information is preferably stored on storage devices such as solid state drives (SSDs) 260, illustratively embodied as flash storage devices, of storage array 150. In an embodiment, the flash storage devices may be based on NAND flash components, e.g., single-layer-cell (SLC) flash, multi-layer-cell (MLC) flash or triple-layer-cell (TLC) flash, although it will be understood to those skilled in the art that other non-volatile, solid-state electronic devices (e.g., drives based on storage class memory components) may be advantageously used with the embodiments described herein. Accordingly, the storage devices may or may not be block-oriented (i.e., accessed as blocks). The storage controller 240 includes one or more ports having I/O interface circuitry that couples to the SSDs 260 over the storage interconnect 140, illustratively embodied as a serial attached SCSI (SAS) topology. Alternatively, other point-to-point I/O interconnect arrangements may be used, such as a conventional serial ATA (SATA) topology or a PCI topology. The system interconnect 270 may also couple the node 200 to a local service storage device 248, such as an SSD, configured to locally store cluster-related configuration information, e.g., as cluster database (DB) 244, which may be replicated to the other nodes 200 in the cluster 100.

The cluster interconnect interface 250 may include one or more ports adapted to couple the node 200 to the other node(s) of the cluster 100. In an embodiment, Ethernet may be used as the clustering protocol and interconnect fabric media, although it will be apparent to those skilled in the art that other types of protocols and interconnects, such as Infiniband, may be utilized within the embodiments described herein. The NVRAM 280 may include a back-up battery or other built-in last-state retention capability (e.g., non-volatile semiconductor memory such as storage class memory) that is capable of maintaining data in light of a failure to the node and cluster environment. Illustratively, a portion of the NVRAM 280 may be configured as one or more non-volatile logs (NVLogs 285) configured to temporarily record (“log”) I/O requests, such as write requests, received from the host 120.

Storage I/O Stack

FIG. 3 is a block diagram of the storage I/O stack 300 that may be advantageously used with one or more embodiments described herein. The storage I/O stack 300 includes a plurality of software modules or layers that cooperate with other functional components of the nodes 200 to provide the distributed storage architecture of the cluster 100. In an embodiment, the distributed storage architecture presents an abstraction of a single storage container, i.e., all of the storage arrays 150 of the nodes 200 for the entire cluster 100 organized as one large pool of storage. In other words, the architecture consolidates storage, i.e., the SSDs 260 of the arrays 150, throughout the cluster (retrievable via cluster-wide keys) to enable storage of the LUNs. Both storage capacity and performance may then be subsequently scaled by adding nodes 200 to the cluster 100.

Illustratively, the storage I/O stack 300 includes an administration layer 310, a protocol layer 320, a persistence layer 330, a volume layer 340, an extent store layer 350, a Redundant Array of Independent Disks (RAID) layer 360, a storage layer 365 and a NVRAM (storing NVLogs) “layer” interconnected with a messaging kernel 370. The messaging kernel 370 may provide a message-based (or event-based) scheduling model (e.g., asynchronous scheduling) that employs messages as fundamental units of work exchanged (i.e., passed) among the layers. Suitable message-passing mechanisms provided by the messaging kernel to transfer information between the layers of the storage I/O stack 300 may include, e.g., for intra-node communication: i) messages that execute on a pool of threads, ii) messages that execute on a single thread progressing as an operation through the storage I/O stack, iii) messages using an Inter Process Communication (IPC) mechanism, and, e.g., for inter-node communication: messages using a Remote Procedure Call (RPC) mechanism in accordance with a function shipping implementation. Alternatively, the I/O stack may be implemented using a thread-based or stack-based execution model. In one or more embodiments, the messaging kernel 370 allocates processing resources from the operating system kernel 224 to execute the messages. Each storage I/O stack layer may be implemented as one or more instances (i.e., processes) executing one or more threads (e.g., in kernel or user space) that process the messages passed between the layers such that the messages provide synchronization for blocking and non-blocking operation of the layers.

In an embodiment, the protocol layer 320 may communicate with the host 120 over the network 130 by exchanging discrete frames or packets configured as I/O requests according to pre-defined protocols, such as iSCSI and FCP. An I/O request, e.g., a read or write request, may be directed to a LUN and may include I/O parameters such as, inter alia, a LUN identifier (ID), a logical block address (LB A) of the LUN, a length (i.e., amount of data) and, in the case of a write request, write data. The protocol layer 320 receives the I/O request and forwards it to the persistence layer 330, which records the request into a persistent write-back cache 380 illustratively embodied as a log whose contents can be replaced randomly, e.g., under some random access replacement policy rather than only in serial fashion, and returns an acknowledgement to the host 120 via the protocol layer 320. In an embodiment only I/O requests that modify the LUN, e.g., write requests, are logged. Notably, the I/O request may be logged at the node receiving the I/O request, or in an alternative embodiment in accordance with the function shipping implementation, the I/O request may be logged at another node.

Illustratively, dedicated logs may be maintained by the various layers of the storage I/O stack 300. For example, a dedicated log 335 may be maintained by the persistence layer 330 to record the I/O parameters of an I/O request as equivalent internal, i.e., storage I/O stack, parameters, e.g., volume ID, offset, and length. In the case of a write request, the persistence layer 330 may also cooperate with the NVRAM 280 to implement the write-back cache 380 configured to store the write data associated with the write request. In an embodiment, the write-back cache may be structured as a log. Notably, the write data for the write request may be physically stored in the cache 380 such that the log 335 contains the reference to the associated write data. It will be understood to persons skilled in the art that other variations of data structures may be used to store or maintain the write data in NVRAM including data structures with no logs. In an embodiment, a copy of the write-back cache may be also maintained in the memory 220 to facilitate direct memory access to the storage controllers. In other embodiments, caching may be performed at the host 120 or at a receiving node in accordance with a protocol that maintains coherency between the data stored at the cache and the cluster.

In an embodiment, the administration layer 310 may apportion the LUN into multiple volumes, each of which may be partitioned into multiple regions (e.g., allotted as disjoint block address ranges), with each region having one or more segments stored as multiple stripes on the array 150. A plurality of volumes distributed among the nodes 200 may thus service a single LUN, i.e., each volume within the LUN services a different LBA range (i.e., offset range and length, hereinafter offset range) or set of ranges within the LUN. Accordingly, the protocol layer 320 may implement a volume mapping technique to identify a volume to which the I/O request is directed (i.e., the volume servicing the offset range indicated by the parameters of the I/O request). Illustratively, the cluster database 244 may be configured to maintain one or more associations (e.g., key-value pairs) for each of the multiple volumes, e.g., an association between the LUN ID and a volume, as well as an association between the volume and a node ID for a node managing the volume. The administration layer 310 may also cooperate with the database 244 to create (or delete) one or more volumes associated with the LUN (e.g., creating a volume ID/LUN key-value pair in the database 244). Using the LUN ID and LBA (or LBA range), the volume mapping technique may provide a volume ID (e.g., using appropriate associations in the cluster database 244) that identifies the volume and node servicing the volume destined for the request as well as translate the LBA (or LBA range) into an offset and length within the volume. Specifically, the volume ID is used to determine a volume layer instance that manages volume metadata associated with the LBA or LBA range. As noted, the protocol layer 320 may pass the I/O request (i.e., volume ID, offset and length) to the persistence layer 330, which may use the function shipping (e.g., inter-node) implementation to forward the I/O request to the appropriate volume layer instance executing on a node in the cluster based on the volume ID.

In an embodiment, the volume layer 340 may manage the volume metadata by, e.g., maintaining states of host-visible containers, such as ranges of LUNs, and performing data management functions, such as creation of snapshots and clones, for the LUNs in cooperation with the administration layer 310. The volume metadata is illustratively embodied as in-core mappings from LUN addresses (i.e., offsets) to durable extent keys, which are unique cluster-wide IDs associated with SSD storage locations for extents within an extent key space of the cluster-wide storage container. That is, an extent key may be used to retrieve the data of the extent at an SSD storage location associated with the extent key. Alternatively, there may be multiple storage containers in the cluster wherein each container has its own extent key space, e.g., where the administration layer 310 provides distribution of extents among the storage containers. An extent is a variable length block of data that provides a unit of storage on the SSDs and that need not be aligned on any specific boundary, i.e., it may be byte aligned. Accordingly, an extent may be an aggregation of write data from a plurality of write requests to maintain such alignment. Illustratively, the volume layer 340 may record the forwarded request (e.g., information or parameters characterizing the request), as well as changes to the volume metadata, in dedicated log 345 maintained by the volume layer 340. Subsequently, the contents of the volume layer log 345 may be written to the storage array 150 in accordance with a checkpoint (e.g., synchronization) operation that stores in-core metadata on the array 150. That is, the checkpoint operation (checkpoint) ensures that a consistent state of metadata, as processed in-core, is committed to (i.e., stored on) the storage array 150; whereas the retirement of log entries ensures that the entries accumulated in the volume layer log 345 synchronize with the metadata checkpoints committed to the storage array 150 by, e.g., retiring those accumulated log entries prior to the checkpoint. In one or more embodiments, the checkpoint and retirement of log entries may be data driven, periodic or both.

In an embodiment, the extent store layer 350 is responsible for storing extents on the SSDs 260 (i.e., on the storage array 150) and for providing the extent keys to the volume layer 340 (e.g., in response to a forwarded write request). The extent store layer 350 is also responsible for retrieving data (e.g., an existing extent) using an extent key (e.g., in response to a forwarded read request). The extent store layer 350 may be responsible for performing de-duplication and compression on the extents prior to storage. The extent store layer 350 may maintain in-core mappings (e.g., embodied as hash tables) of extent keys to SSD storage locations (e.g., offset on an SSD 260 of array 150). The extent store layer 350 may also maintain a dedicated log 355 of entries that accumulate requested “put” and “delete” operations (i.e., write requests and delete requests for extents issued from other layers to the extent store layer 350), where these operations change the in-core mappings (i.e., hash table entries). Subsequently, the in-core mappings and contents of the extent store layer log 355 may be written to the storage array 150 in accordance with a “fuzzy” checkpoint 390 (i.e., checkpoints with incremental changes recorded in one or more log files) in which selected in-core mappings (less than the total), are committed to the array 150 at various intervals (e.g., driven by an amount of change to the in-core mappings, size thresholds of log 355, or periodically). Notably, the accumulated entries in log 355 may be retired once all in-core mappings have been committed to include the changes recorded in those entries.

In an embodiment, the RAID layer 360 may organize the SSDs 260 within the storage array 150 as one or more RAID groups (e.g., sets of SSDs) that enhance the reliability and integrity of extent storage on the array by writing data “stripes” having redundant information, i.e., appropriate parity information with respect to the striped data, across a given number of SSDs 260 of each RAID group. The RAID layer 360 may also store a number of stripes (e.g., stripes of sufficient depth), e.g., in accordance with a plurality of contiguous range write operations, so as to reduce data relocation (i.e., internal flash block management) that may occur within the SSDs as a result of the operations.

In an embodiment, the storage layer 365 implements storage I/O drivers that may communicate directly with hardware (e.g., the storage controllers and cluster interface) cooperating with the operating system kernel 224, such as a Linux virtual function I/O (VFIO) driver.

Write Path

FIG. 4 illustrates an I/O (e.g., write) path 400 of the storage I/O stack 300 for processing an I/O request, e.g., a SCSI write request 410. The write request 410 may be issued by host 120 and directed to a LUN stored on the storage arrays 150 of the cluster 100. Illustratively, the protocol layer 320 receives and processes the write request by decoding 420 (e.g., parsing and extracting) fields of the request, e.g., LUN ID, LBA and length (shown at 413), as well as write data 414. The protocol layer 320 may use the results 422 from decoding 420 for a volume mapping technique 430 (described above) that translates the LUN ID and LBA range (i.e., equivalent offset and length) of the write request to an appropriate volume layer instance, i.e., volume ID (volume 445), in the cluster 100 that is responsible for managing volume metadata for the LBA range. In an alternative embodiment, the persistence layer 330 may implement the above described volume mapping technique 430. The protocol layer then passes the results 432, e.g., volume ID, offset, length (as well as write data), to the persistence layer 330, which records the request in the persistence layer log 335 and returns an acknowledgement to the host 120 via the protocol layer 320. The persistence layer 330 may aggregate and organize write data 414 from one or more write requests into a new extent 610 and perform a hash computation, i.e., a hash function, on the new extent to generate a hash value 472 in accordance with an extent hashing technique 450.

The persistence layer 330 may then pass the write request with aggregated write data including, e.g., the volume ID, offset and length, as parameters 434 to the appropriate volume layer instance. In an embodiment, message passing of the parameters 434 (received by the persistence layer) may be redirected to another node via the function shipping mechanism, e.g., RPC, for inter-node communication. Alternatively, message passing of the parameters 434 may be via the IPC mechanism, e.g., message threads, for intra-node communication.

In one or more embodiments, a bucket mapping technique 476 is provided that translates the hash value 472 to an instance of an appropriate extent store layer (i.e., extent store instance 470) that is responsible for storing the new extent 610. Note, the bucket mapping technique may be implemented in any layer of the storage I/O stack above the extent store layer. In an embodiment, for example, the bucket mapping technique may be implemented in the persistence layer 330, the volume layer 340, or a layer that manages cluster-wide information, such as a cluster layer (not shown). Accordingly, the persistence layer 330, the volume layer 340, or the cluster layer may contain computer executable instructions executed by the CPU 210 to perform operations that implement the bucket mapping technique 476 described herein. The persistence layer 330 may then pass the hash value 472 and the new extent 610 to the appropriate volume layer instance and onto the appropriate extent store instance via an extent store put operation. The extent hashing technique 450 may embody an approximately uniform hash function to ensure that any random extent to be written may have an approximately equal chance of falling into any extent store instance 470, i.e., hash buckets are distributed across extent store instances of the cluster 100 based on available resources. As a result, the bucket mapping technique 476 provides load-balancing of write operations (and, by symmetry, read operations) across nodes 200 of the cluster, while also leveling flash wear in the SSDs 260 of the cluster.

In response to the put operation, the extent store instance may process the hash value 472 to perform an extent metadata selection technique 460 that (i) selects an appropriate hash table 480 (e.g., hash table 480a) from a set of hash tables (illustratively in-core) within the extent store instance 470, and (ii) extracts a hash table index 462 from the hash value 472 to index into the selected hash table and lookup a table entry having an extent key 475 identifying a storage location 490 on SSD 260 for the extent. Accordingly, the extent store layer 350 contains computer executable instructions executed by the CPU 210 to perform operations that implement the extent metadata selection technique 460 described herein. If a table entry with a matching extent key is found, then the SSD location 490 mapped from the extent key 475 is used to retrieve an existing extent (not shown) from SSD. The existing extent is then compared with the new extent 610 to determine whether their data is identical. If the data is identical, the new extent 610 is already stored on SSD 260 and a de-duplication opportunity (denoted de-duplication 452) exists such that there is no need to write another copy of the data. Accordingly, a reference count in the table entry for the existing extent is incremented and the extent key 475 of the existing extent is passed to the appropriate volume layer instance for storage within an entry (denoted as volume metadata entry 446) of a dense tree metadata structure 444 (e.g., dense tree 444a), such that the extent key 475 is associated an offset range 440 (e.g., offset range 440a) of the volume 445.

However, if the data of the existing extent is not identical to the data of the new extent 610, a collision occurs and a deterministic algorithm is invoked to sequentially generate as many new candidate extent keys (not shown) mapping to the same bucket as needed to either provide de-duplication 452 or to produce an extent key that is not already stored within the extent store instance. Notably, another hash table (e.g. hash table 480n) may be selected by a new candidate extent key in accordance with the extent metadata selection technique 460. In the event that no de-duplication opportunity exists (i.e., the extent is not already stored) the new extent 610 is compressed in accordance with compression technique 454 and passed to the RAID layer 360, which processes the new extent 610 for storage on SSD 260 within one or more stripes 710 of RAID group 820. The extent store instance may cooperate with the RAID layer 360 to identify a storage segment 650 (i.e., a portion of the storage array 150) and a location on SSD 260 within the segment 650 in which to store the new extent 610. Illustratively, the identified storage segment is a segment with a large contiguous free space having, e.g., location 490 on SSD 260b for storing the extent 610.

In an embodiment, the RAID layer 360 then writes the stripes 710 across the RAID group 820, illustratively as one or more full stripe writes 458. The RAID layer 360 may write a series of stripes 710 of sufficient depth to reduce data relocation that may occur within the flash-based SSDs 260 (i.e., flash block management). The extent store instance then (i) loads the SSD location 490 of the new extent 610 into the selected hash table 480n (i.e., as selected by the new candidate extent key), (ii) passes a new extent key (denoted as extent key 475) to the appropriate volume layer instance for storage within an entry (also denoted as volume metadata entry 446) of a dense tree 444 managed by that volume layer instance, and (iii) records a change to extent metadata of the selected hash table in the extent store layer log 355. Illustratively, the volume layer instance selects dense tree 444a spanning an offset range 440a of the volume 445 that encompasses the offset range of the write request. As noted, the volume 445 (e.g., an offset space of the volume) is partitioned into multiple regions (e.g., allotted as disjoint offset ranges); in an embodiment, each region is represented by a dense tree 444. The volume layer instance then inserts the volume metadata entry 446 into the dense tree 444a and records a change corresponding to the volume metadata entry in the volume layer 345. Accordingly, the I/O (write) request is sufficiently stored on SSD 260 of the cluster.

Read Path

FIG. 5 illustrates an I/O (e.g., read) path 500 of the storage I/O stack 300 for processing an I/O request, e.g., a SCSI read request 510. The read request 510 may be issued by host 120 and received at the protocol layer 320 of a node 200 in the cluster 100. Illustratively, the protocol layer 320 processes the read request by decoding 420 (e.g., parsing and extracting) fields of the request, e.g., LUN ID, LBA, and length (shown at 513), and uses the decoded results 522, e.g., LUN ID, offset, and length, for the volume mapping technique 430. That is, the protocol layer 320 may implement the volume mapping technique 430 (described above) to translate the LUN ID and LBA range (i.e., equivalent offset and length) of the read request to an appropriate volume layer instance, i.e., volume ID (volume 445), in the cluster 100 that is responsible for managing volume metadata for the LBA (i.e., offset) range. The protocol layer then passes the results 532 to the persistence layer 330, which may search the write cache 380 to determine whether some or all of the read request can be serviced from its cached data. If the entire request cannot be serviced from the cached data, the persistence layer 330 may then pass the remaining portion of the request including, e.g., the volume ID, offset and length, as parameters 534 to the appropriate volume layer instance in accordance with the function shipping mechanism, (e.g., RPC, for inter-node communication) or the IPC mechanism, (e.g., message threads, for intra-node communication).

The volume layer instance may process the read request to access a dense tree metadata structure 444 (e.g., dense tree 444a) associated with a region (e.g., offset range 440a) of a volume 445 that encompasses the requested offset range (specified by parameters 534). The volume layer instance may further process the read request to search for (lookup) one or more volume metadata entries 446 of the dense tree 444a to obtain one or more extent keys 475 associated with one or more extents 610 (or portions of extents) within the requested offset range. In an embodiment, each dense tree 444 may be embodied as multiple levels of a search structure with possibly overlapping offset range entries at each level. The various levels of the dense tree may have volume metadata entries 446 for the same offset, in which case, the higher level has the newer entry and is used to service the read request. A top level of the dense tree 444 is illustratively resident in-core and a page cache 448 may be used to access lower levels of the tree. If the requested range or portion thereof is not present in the top level, a metadata page associated with an index entry at the next lower tree level (not shown) is accessed. The metadata page (i.e., in the page cache 448) at the next level is then searched to find any overlapping entries. This process is then iterated until one or more volume metadata entries 446 of a level are found to ensure that the extent key(s) 475 for the entire requested read range are found. If not metadata entries exist for the entire or portions of the requested range, then the missing portion(s) are zero filled.

Once found, each extent key 475 is processed by the volume layer 340 to, e.g., implement the bucket mapping technique 476 that translates the extent key to an appropriate extent store instance 470 responsible for storing the requested extent 610. Note that, in an embodiment, each extent key 475 may be substantially identical to the hash value 472 associated with the extent 610, i.e., the hash value as calculated during the write request for the extent, such that the bucket mapping 476 and extent metadata selection 460 techniques may be used for both write and read path operations. Note also that the extent key 475 may be derived from the hash value 472. The volume layer 340 may then pass the extent key 475 (i.e., the hash value from a previous write request for the extent) to the appropriate extent store instance 470 (via an extent store get operation), which performs an extent key-to-SSD mapping to determine the location on SSD 260 for the extent.

In response to the get operation, the extent store instance may process the extent key 475 (i.e., hash value 472) to perform the extent metadata selection technique 460 that (i) selects an appropriate hash table 480 (e.g., hash table 480a) from a set of hash tables within the extent store instance 470, and (ii) extracts a hash table index 462 from the extent key 475 (i.e., hash value 472) to index into the selected hash table and lookup a table entry having a matching extent key 475 that identifies a storage location 490 on SSD 260 for the extent 610. That is, the SSD location 490 mapped to the extent key 475 may be used to retrieve the existing extent (denoted as extent 610) from SSD 260 (e.g., SSD 260b). The extent store instance then cooperates with the RAID layer 360 to access the extent on SSD 260b and retrieve the data contents in accordance with the read request. Illustratively, the RAID layer 360 may read the extent in accordance with an extent read operation 468 and pass the extent 610 to the extent store instance. The extent store instance may then decompress the extent 610 in accordance with a decompression technique 456, although it will be understood to those skilled in the art that decompression can be performed at any layer of the storage I/O stack 300. The extent 610 may be stored in a buffer (not shown) in memory 220 and a reference to that buffer may be passed back through the layers of the storage I/O stack. The persistence layer may then load the extent into a read cache 580 (or other staging mechanism) and may extract appropriate read data 512 from the read cache 580 for the LBA range of the read request 510. Thereafter, the protocol layer 320 may create a SCSI read response 514, including the read data 512, and return the read response to the host 120.

Layered File System

The embodiments described herein illustratively employ a layered file system of the storage I/O stack. The layered file system includes a flash-optimized, log-structured layer (i.e., extent store layer) of the file system configured to provide sequential storage of data and metadata (i.e., log-structured layout) on the SSDs 260 of the cluster. The data may be organized as an arbitrary number of variable-length extents of one or more host-visible LUNs served by the nodes. The metadata may include mappings from host-visible logical block address ranges (i.e., offset ranges) of a LUN to extent keys, as well as mappings of the extent keys to SSD storage locations of the extents. Illustratively, the volume layer of the layered file system cooperates with the extent store layer to provide a level of indirection that facilitates efficient log-structured layout of extents on the SSDs by the extent store layer.

In an embodiment, functions of the log-structured layer of the file system, such as write allocation and flash device (i.e., SSD) management, are performed and maintained by the extent store layer 350. Write allocation may include gathering of the variable-length extents to form full stripes that may be written to free segments across SSDs of one or more RAID groups, whereas flash device management may include segment cleaning to create such free segments that indirectly map to the SSDs. Instead of relying on garbage collection in the SSDs, the storage I/O stack may implement segment cleaning (i.e., garbage collection) in the extent store layer to bypass performance impacts of flash translation layer (FTL) functionality (including garbage collection) in the SSD. In other words, the storage I/O stack allows the log-structured layer of the file system to operate as a data layout engine using segment cleaning to effectively replace the FTL functionality of the SSD. The extent store layer may thus process random write requests in accordance with segment cleaning (i.e., garbage collection) to predict flash behavior within its FTL functionality. As a result, a log-structured equivalent source of write amplification for the storage I/O stack may be consolidated and managed at the extent store layer. In addition, the log-structured layer of the file system may be employed, in part, to improve write performance from the flash devices of the storage array.

Segment Cleaning

FIG. 6 illustrates segment cleaning by the layered file system. In an embodiment, the extent store layer 350 of the layered file system may write extents to an empty or free region or “segment.” Before rewriting that segment again, the extent store layer 350 may clean the segment in accordance with segment cleaning which, illustratively, may be embodied as a segment cleaning process. The segment cleaning process may read all valid extents 610 from an old segment 650a and write those valid extents (i.e., extents not deleted or overwritten 612) to one or more new segments 650b-c, to thereby free-up (i.e., “clean”) the old segment 650a. New extents may then be written sequentially to the old (now clean) segment. The layered file system may maintain a certain amount of reserve space (i.e., free segments) to enable efficient performance of segment cleaning. For example, the layered file system may illustratively maintain a reserve space of free segments equivalent to approximately 7% of storage capacity. The sequential writing of new extents may manifest as full stripe writes 458, such that a single write operation to storage spans all SSDs in a RAID group 820. Write data may be accumulated until a stripe write operation of a minimum depth can be made.

Illustratively, segment cleaning may be performed to free one or more selected segments that indirectly map to SSDs. As used herein, a SSD may be composed of a plurality of segment chunks 620, wherein each chunk is illustratively approximately 1 GB in size. A segment may include a segment chunk 620a-c from each of a plurality of SSDs in a RAID group 820. Thus, for a RAID group having 24 SSDs, wherein 22 SSDs store data (data SSDs) and 2 SSDs store parity (parity SSDs), each segment may include 22 GB of data and 2 GB of parity. The RAID layer may further configure the RAID groups according to one or more RAID implementations, e.g., RAID 1, 4, 5 and/or 6, to thereby provide protection over the SSDs in the event of, e.g., failure to one or more SSDs. Notably, each segment may be associated with a different RAID group and, thus, may have a different RAID configuration, i.e., each RAID group may be configured according to a different RAID implementation. To free-up or clean selected segments, extents of the segments that contain valid data are moved to different clean segments and the selected segments (now clean) are freed for subsequent reuse. Segment cleaning consolidates fragmented free space to improve write efficiency, e.g., to underlying flash blocks by reducing performance impacts of the FTL. Once a segment is cleaned and designated freed, data may be written sequentially to that segment. Accounting structures, e.g., free segment maps or an amount of segment free space, maintained by the extent store layer for write allocation, may be employed by the segment cleaning process. Notably, selection of a clean segment to receive data (i.e., writes) from a segment being cleaned may be based upon the amount of free space remaining in the clean segment and/or the last time the clean segment was used. Note further that different portions of data from the segment being cleaned may be moved to different “target” segments. That is, a plurality of relatively clean segments 650b,c may receive differing portions of data from the segment 650a being cleaned.

Illustratively, segment cleaning may cause some write amplification in the storage array (SSDs). However, the file system may reduce such write amplification by writing extents to the SSDs sequentially as a log device. For example, given SSDs with an erase block size of approximately 2 MBs, by writing at least 2 MB of data (extents) sequentially to a free segment, an entire erase block may be overwritten and fragmentation at the SSD level may be eliminated (i.e., reducing garbage collection in the SSD). Yet, the SSDs typically stripe data across multiple flash components and across multiple channels (i.e., storage controllers 240) in order to realize performance. Thus, a relatively large (e.g., 1 GB) write granularity to a free (i.e., clean) segment may be necessary to avoid write amplification at the SSD level (i.e., to override internal SSD striping).

In an embodiment, because the erase block boundaries in the SSD may be unknown, the write granularity should be large enough so that a sequence of writes for extents over a large contiguous range may overwrite previously written extents on the SSD and effectively override garbage collection in the SSDs. In other words, such garbage collection may be preempted because the new data is written over the same range as previous data such that the new data completely overwrites the previously written data. This approach also avoids consuming the reserve space capacity with the new write data. Accordingly, an advantage of the log-structured feature of the storage I/O stack (i.e., log-structured layer of the file system) is the ability to reduce write amplification of the SSDs with only a minimum amount of reserve space in the SSDs. This log-structured feature effectively “moves” flash device management of reserve space from the SSD to the extent store layer, which uses that reserve space to manage the write amplification. Thus, instead of having two sources of write amplification (i.e., the extent store layer and the SSD FTL, which multiply) there is only one source of write amplification (i.e., the extent store layer).

Write Allocation

In an embodiment, there may be multiple RAID stripes per segment. Each time a segment is allocated, i.e., after cleaning the segment, the chunks of various SSDs within the segment may include a series of RAID stripes, each aligned by extent. The chunks may be at the same or different offsets within the SSDs. The extent store layer may read the chunks sequentially for cleaning purposes and relocate all the valid data to another segment. Thereafter, the chunks 620 of the cleaned segment may be freed and a decision may be rendered as to how to constitute the next segment that uses the chunks. For example, if a SSD is removed from a RAID group, a portion (i.e., a set of chunks 620) of capacity may be omitted from the next segment (i.e., change in RAID stripe configuration) so as to constitute the RAID group from a plurality of chunks 620 that is one chunk narrower, i.e., making the RAID width one less. Thus, by using segment cleaning, a RAID group of the chunks 620 constituting the segments may be effectively created each time a new segment is allocated, i.e., a RAID group is created dynamically from available SSDs when a new segment is allocated. There is generally no requirement to include all of the SSDs 260 in the storage array 150 in the new segment. Alternatively, a chunk 620 from a newly introduced SSD can be added into a RAID group created when a new segment 650 is allocated.

FIG. 7a illustrates a RAID stripe formed by the layered file system. As noted, write allocation may include gathering of the variable-length extents to form one or more stripes across SSDs of one or more RAID groups. In an embodiment, the RAID layer 360 may manage parity computations and topology information used for placement of the extents 610 on the SSDs 260a-n of the RAID group 820. To that end, the RAID layer may cooperate with the extent store layer to organize the extents as stripes 710 within the RAID group. Illustratively, the extent store layer may gather the extents 610 to form one or more full stripes 710 that may be written to a free segment 650a such that a single stripe write operation 458 may span all SSDs in that RAID group. The extent store layer may also cooperate with the RAID layer to pack each stripe 710 as a full stripe of variable-length extents 610. Once the stripe is complete, the RAID layer may pass the full stripe 710 of extents as a set of chunks 620d-f to the storage layer 365 of the storage I/O stack for storage on the SSDs 260. By writing a full stripe (i.e., data and parity) to the free segment, the layered file system avoids the cost of parity updates and spreads any required read operation load across the SSDs.

In an embodiment, an extent store instance may be viewed as a global pool of extents stored on the storage arrays 150 of the cluster, where each extent may be maintained within a RAID group 820 of an extent store. Assume one or more variable-length (i.e., small and/or large) extents are written to a segment. The extent store layer may gather the variable-length extents to form one or more stripes across the SSDs of the RAID group. Although each stripe may include multiple extents 610 and an extent 610c could span more than one stripe 710a,b, each extent is entirely stored on one SSD. In an embodiment, a stripe may have a depth of 16 KB and an extent may have a size of 4 KB, but the extent may thereafter be compressed down to 1 or 2 KB or smaller permitting a larger extent to be packed which may exceed the stripe depth (i.e., the chunk 620g depth). Thus, a stripe may constitute only part of the extent, so the depth of the stripe 710 (i.e., the set of chunks 620d-f constituting the stripe) may be independent of the extent(s) written to any one SSD. Since the extent store layer may write the extents as full stripes across one or more free segments of the SSDs, write amplification associated with processing information of the stripes may be reduced.

Segment-Based RAID Configuration

The embodiments described herein are directed to storing RAID-related metadata on a distributed database of the cluster (i.e., the cluster database), which is separate and apart from a storage array of SSDs configured to serve as main storage for the nodes of the cluster. The RAID-related metadata may be embodied as RAID labels (or other data structures) having configuration information that identifies one or more RAID groups associated with an extent store and storage devices, e.g., SSDs, within each RAID group. In turn, each segment of the extent store may be associated with a RAID group (and its associated data structures). Accordingly, each segment may have a different (i.e., its own) RAID configuration as the storage pool changes (i.e., addition and/or removal of SSDs).

FIG. 7b illustrates changes to a segment-based RAID configuration in accordance with changes to the storage pool of the cluster. Illustratively, a plurality of segments 650a,b is associated with a RAID group 820a having a set of SSDs 260a-n. A new (i.e., additional or replacement) SSD 260n+1 may be added to the storage array such that existing segments 650a,b include SSDs 260a-n, while a new segment 650c includes SSDs 260a-n in addition to SSD 260n+1. As the segments 650a,b are cleaned, data is written (i.e., moved) from those segments to the segment 650c. A new RAID group 820b may be created once new segment 650c is allocated. The layered file system may then write extents to the new segment 650c with the additional capacity available for immediate use either to recover from a degraded level of redundancy or to store additional data.

Each SSD may be part of a RAID configuration topology tree structure that defines an extent store using a multi-level hierarchy (e.g., three levels), wherein the first level of the tree identifies the SSD, the second level identifies the RAID group to which the SSD belongs, and the third level identifies the extent store or storage container to which the RAID group belongs. FIG. 8 illustrates a RAID configuration topology tree structure. An extent store 805 may include one or more RAID groups 820a-n, each of which may include one or more SSDs 260. Illustratively, each RAID group may be associated with at least one segment of the extent store. That is, a plurality of segments may be associated with the same RAID group, so that RAID groups may vary by segment (or remain the same), i.e., segment-based RAID configuration. Accordingly, SSDs of the storage array are assimilated into RAID groups by segment. As noted, RAID groups are illustratively created when segments are allocated.

Predictive Failure

The embodiments described herein provide a technique for predicting failure of the storage devices, e.g., the SSDs 260, of the storage array and for establishing one or more threshold conditions for replacing each of those SSDs. To that end, the predictive technique periodically monitors soft and hard failures of the SSDs, e.g., Self-Monitoring, Analysis and Reporting Technology (SMART), as well as various usage counters pertaining to I/O workloads and response times of the SSDs. A heuristic procedure may then be performed that combines the monitored results to calculate the predicted failure and recommend (e.g., via an alert) replacement of the SSDs, using the threshold conditions based on the current usage and failure patterns of the SSDs. In addition, one or more policies may be provided for replacing the SSDs of the storage array in a cost-effective manner that ensures non-disruptive operation and/or replacement of the SSDs, while obviating a potential catastrophic scenario based on the usage and failure patterns of the SSDs, e.g., deterioration of redundancy resulting in data loss. As for the latter, the predictive technique may provide a “warning” (e.g., an alert) to enable replacement of the SSDs prior to such catastrophic failure while maintaining an expected level of reliability for data stored in the storage array. That is, a schedule of staged replacement of SSDs may be provided prior to a catastrophic failure such that an expected (e.g., minimum) level of redundancy (e.g., RAID 6) is maintained for the storage array.

The predictive technique described herein may apply to any flash-based storage system where “wear-leveling” of the drives of the storage array is expected to be approximately even such that a substantial number of the drives may be expected to fail within generally the same period of time. That is, assuming all the SSDs perform according to their vendor/manufacturing specifications, the wear leveling pattern is expected to be sufficiently identical within each SSD and uniform across all the SSDs of the storage array, such that the SSDs may fail together around the same time. The technique, thus, provides preventative measures in view of observed (and reported) errors which, if not pursued, may result in an onerous situation for a system administrator or cause a catastrophic failure beyond the redundancy ability of the storage array. It should be noted that the errors observed (e.g., SMART data) in accordance with the predictive technique are SSD-based errors that facilitate the predictive analysis (i.e., predicted failure) and determinations described herein. However, it should be further noted that the predictive technique may apply to a storage system where the drives do not generally fail at the same time, e.g., storage device failure may not necessarily occur from wear leveling of the medium, but from other reasons, such as electro-mechanical failures.

FIG. 9 is a flow diagram of a technique for predicting failure of one or more storage devices serviced by a node of the cluster. In an embodiment, a heath monitor module (health monitor 910) of the storage I/O stack 300 executes periodically (e.g., at 10 minute intervals) to monitor the SSDs in response to a message via the messaging kernel 370 from a timer service 925. Illustratively, upon each “wakeup” (i.e., periodic execution of the health monitor), a threshold handler module (threshold handler 920) reads SMART data 930 from each SSD 260 of the storage array and executes the heuristic procedure to determine whether the respective SSD is predicted to fail. To that end, the threshold handler 920 may maintain (in memory) a set of counters 950, one for each SSD, such that during I/O operations 940a-c (e.g., SATA I/O operations over storage interconnect 140) by the storage layer 365, a number of detected errors may be recorded (i.e., counted) in the set of counters corresponding with each respective SSD based on a type of detected I/O error (e.g., SATA I/O sense code error). For example, a first type of detected I/O error (e.g., SATA I/O sense key=1 during I/O operation 940a) may be used to count (e.g., counter 952) a number of recovered errors by the SSD; a second type of detected I/O error (e.g., SATA I/O sense key=3 during I/O operation 940b) may be used to count (e.g., counter 954) a number of medium (i.e., flash component) errors by the SSD; and a third type of detected I/O error (e.g., failure of the SSD to respond within a specified time during I/O operation 940c) may be used to count (e.g., counter 956) a number of timeout errors.

As such, the heuristic procedure may compare a value of each counter 952-956 to an associated counter threshold value 962a-966a over an associated interval 962b-966b to predict failure of the corresponding SSD. If the counter exceeds the associated counter threshold over the associated interval, the corresponding SSD is predicted to fail and recommended for replacement by issuing an alert 980. For example, if the recovered errors 952 exceeds a counter threshold value 962a (e.g., 25) over an interval 962b (e.g., 10 minutes), an alert is issued to replace the corresponding SSD. Notably, multiple different counter threshold value/interval pairs may be associated with a given counter and compared by the heuristic procedure. For example, the recovered errors 952 also may be compared to a counter threshold value 964a (e.g., 100) over an interval 964b (e.g., 7 days) to predict failure of the corresponding SSD. Further, illustratively, one or more counters may be reset if no additional error associated with the counter is detected during the interval. For instance, the recovered errors counter 952 may be reset if no recovered error is detected during the interval 962b (e.g., 10 minutes). In addition, the heuristic procedure may compare certain counters (e.g., timeout errors 956) based on the type of flash components (e.g., MLC) in the SSD.

In an embodiment, the read SMART data 930 may include SMART attributes, such as power-on hours 932, indicating a count of a number of hours (minutes, or seconds) that the corresponding SSD is in power-on state; temperature 934, indicating air temperature inside a housing of the corresponding SSD; reassigned block count 936, indicating a number of defective blocks detected in the corresponding SSD; and a used reserved block count 938, indicating a number of reserved blocks used in the corresponding SSD. Illustratively, the heuristic procedure compares each read SMART data attribute 932-938 to an associated SMART threshold 970. If the attribute exceeds the associated SMART threshold (deemed a “SMART trip”), the corresponding SSD is predicted to fail and recommended for replacement by issuing an alert. Notably, the SMART thresholds may be normalized values based on characteristics (e.g., flash component type and total storage capacity) of the SSD. For example, a SMART threshold value of 8007 may be associated with the reassigned block count of a 480 GB SSD having TLC flash components. In addition, the health monitor 910 may, illustratively, power down SSDs whose power-on hours SMART attribute exceeds a fraction (e.g., 95%) of the associated SMART threshold 970 for power-on hours.

In an embodiment, the counter thresholds 960 and SMART threshold 970 are selected so that the heuristic procedure (i.e., threshold handler 920) may predict failure prior actual failure (i.e., end of life). Accordingly, the SSD predicted to fail may be placed in Read Only Service (ROS) so that extents stored on the SSD may be migrated (e.g., via segment cleaning) to another SSD in the storage array. Illustratively, the thresholds are chosen so that the predicted failure of the SSD occurs permitting a migration period (e.g., one to three months) prior to actual failure of the SSD. For example, a non-disruptive replacement of the SSDs may be performed by replacing all of the SSDs (e.g., all 24 drives) at the same time, through data migration. Data migration may be realized by cleaning segments (i.e., copying data) from one or more failing SSDs to new SSDs at once, with the goal of non-disruption to a customer. However, a new set of drives (and one or more shelves housing the drives) are provided at once with a concomitant cost. Additional storage controllers 240 as well as additional links for the storage interconnect 140 may be needed. Further, disruption (e.g., performance degradation) may occur as data on all drives (e.g., failing 24 SSDs) is migrated at once to a new set of drives (e.g., replacement 24 SSDs).

In an embodiment, a first policy implemented by a policy handler module (policy handler 922) allows for gradual (i.e., staggered) replacements of the drives. For example, if the storage array 150 has one or two shelves 990a,b of SSDs 260, it may be desirable to implement the first policy wherein data is migrated gradually to reduce performance disruptions (i.e., overhead of data migration by segment cleaning) and cost (i.e., frequent drive replacement) issues to the customer. Accordingly, a customer (i.e., administrator) may be warned (i.e., alerted) over a predetermined time window (i.e., the migration period) after the predicted failure of the SSDs, during which the customer may be directed to gradually migrate data to a new shelf. The gradual migration may be embodied as a staged (i.e., arranged) replacement policy where, e.g., one drive is replaced per week over the time window.

In an embodiment, a non-disruptive replacement of the SSDs may be performed by gradually replacing all of the SSDs (e.g., one at a time), illustratively through cleaning segments (i.e., copying data) from the one or more predictively failing SSDs to new (replacement) SSDs, with the goal of non-disruption to a customer. That is, segments on the (predictively) failing SSDs in a (potentially) failing RAID group 820 may be cleaned, thereby copying data to a second new (reliable) RAID group having the new (replacement) SSDs. Migration may be started sufficiently early so that any expected (i.e., predicted) failure of SSDs in the potentially failing RAID group does not fatally deteriorate redundancy of that RAID group during the migration. That is, for a time when a RAID group 820 is predicted to fail (i.e., migration period after a failure alert), data migration may be started, so that the migration to a new RAID group completes prior to the actual failure date. As used herein, the predicted failure (i.e., end-of-life) time of the RAID group may be a future time, after the migration period following the predicted failure alert, at which the RAID group redundancy is expected to degenerate (i.e., deteriorate) below a user-defined level of redundancy.

For example, the user may specify that a RAID group having a RAID 6 level of redundancy should not degenerate below a RAID 5 level of redundancy during a predictive drive failure migration. In this case, a single drive from the RAID group may be replaced at a time (temporarily degenerating the RAID group from RAID 6 to RAID 5) as data is migrated (i.e., segments cleaned) and full redundancy is restored (RAID 6). Accordingly, as each drive of the RAID group is replaced prior to the (original) predicted RAID group failure time, data in the replaced drives may be migrated to the replaced (new) drives. As drives are replaced and/or unexpected drive failures occur, the predicted failure time of the RAID group may be re-determined (i.e., recalculated). As a result, the interval of drive replacement may change, e.g., an original schedule of weekly drive replacement intervals may become every 3 days.

Depending on the redundancy (e.g., RAID) configuration, a second policy implemented by the policy handler 922 may leverage the fact that the storage array may sustain up to two (2) drive failures until the data of the failed drives is reconstructed on replacement drives. This second policy enables replacement of one or two SSDs at a time on the shelf. The predictive technique (which is illustratively implemented as a process of the storage layer of the storage I/O stack) cooperates with the RAID layer to reconstruct the failed drives using the storage capacity of the remaining drives. The failed drive(s) may then be replaced with the new drive(s) and reconstructed data may be migrated on to the new drive(s). Thereafter, a “planned” migration operation may be utilized for remaining drives of the shelf.

In an embodiment, a third policy implemented by the policy handler 922 may specify replacement of all the drives and an entire shelf at the same time. Here, data may be migrated from an “old” shelf to a “new” replacement shelf prior to retirement of the old shelf.

The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software encoded on a tangible (non-transitory) computer-readable medium (e.g., disks, electronic memory, and/or CDs) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the embodiments herein.

Furthermore, it will be understood to those skilled in the art that the implementations described herein may apply to any type of special-purpose (e.g., file server, filer or storage serving appliance) or general-purpose computer, including a standalone computer or portion thereof, embodied as or including a storage system. Those of skill in the art would further appreciate that the various illustrative modules, algorithms and method steps of implementations described herein may be implemented as computer electronic hardware, computer software, or combinations of both to obtain a given functionality. Illustratively, interchangeability of hardware and software may be made to obtain the same functionality. Skilled artisans may implement the described functionality in varying ways, but such design choices should not be interpreted as causing departure from the implementations described herein.

Accordingly this description is to be taken only by way of example and not to otherwise limit the scope of the aspects of the disclosure. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the subject matter.

Claims

1. A system comprising:

a processor of a storage system coupled to a plurality of storage devices; and

a memory coupled to the processor and configured to store a storage input/output (I/O) stack executable by the processor, the storage I/O stack configured to periodically monitor results of soft and hard failures of the storage devices, the storage I/O stack further configured to perform a heuristic procedure that combines the monitored results to calculate a predicted failure and recommend replacement of the storage devices using threshold conditions based on current usage and failure patterns of the storage devices.

2. The system of claim 1 wherein the storage I/O stack includes a health monitor configured to monitor the storage devices in response to a message from a time service of the storage I/O stack.

3. The system of claim 1 wherein the storage devices are solid state drives (SSDs) and wherein the results of the soft and hard failures are errors detected at the SSDs that facilitate a predictive analysis of failure of the SSDs.

4. The system of claim 3 wherein the storage I/O stack includes a threshold handler configured to read the detected errors from the SSDs and perform the heuristic procedure to determine whether a respective SSD is predicted to fail.

5. The system of claim 4 wherein the threshold handler is further configured to maintain a set of counters for each SSD, and wherein the set of counters is configured to record the detected errors based on a type of detected error.

6. The system of claim 5 wherein the heuristic procedure is configured to:

compare a value of each counter to an associated counter threshold value over an associated interval to predict failure of the respective SSD; and

in response to the respective counter exceeding the associated counter threshold value over the associated interval, predict failure of the respective SSD and recommend replacement of the respective SSD.

7. The system of claim 1 wherein the storage I/O stack includes a policy handler configured to implement one or more policies for replacing the storage devices in a manner that ensures non-disruptive operation and replacement of the storage devices.

8. The system of claim 7 wherein the one or more policies include a policy that provides a gradual staged replacement of the storage devices over a predetermined time window.

9. The system of claim 7 wherein the one or more polices include a policy that enables replacement of one or two storage devices at a time on a storage array configured to sustain up to two storage device failures until data on the failed storage devices is reconstructed on replacement storage devices.

10. The system of claim 7 wherein the one or more policies include a policy that specifies replacement of all of the storage devices on a shelf of a storage array at a same time.

11. A method comprising:

executing, by a processor coupled to a plurality of storage devices, a storage input/output (I/O) stack including one or more modules configured to predict failure of the storage devices and establish threshold conditions for replacing the storage devices;

periodically monitoring results of soft and hard failures of the storage devices;

performing a heuristic procedure that combines the monitored results to calculate a predicted failure; and

recommending replacement of the storage devices using the threshold conditions based on current usage and failure patterns of the storage devices.

12. The method of claim 11 further comprising monitoring the storage devices in response to a message from a time service of the storage I/O stack.

13. The method of claim 11 wherein the storage devices are solid state drives (SSDs) and wherein the results of the soft and hard failures are errors detected at the SSDs that facilitate a predictive analysis of failure of the SSDs.

14. The method of claim 13 further comprising:

reading the detected errors from the SSDs; and

performing the heuristic procedure to determine whether a respective SSD is predicted to fail.

15. The method of claim 14 further comprising:

maintaining a set of counters for each SSD; and

recording the detected errors on the set of counters based on a type of detected error.

16. The method of claim 15 further comprising:

comparing a value of each counter to an associated counter threshold value over an associated interval to predict failure of the respective SSD according to the heuristic procedure; and

in response to the respective counter exceeding the associated counter threshold value over the associated interval, predicting failure of the respective SSD and recommending replacement of the respective SSD.

17. The method of claim 11 further comprising:

implementing one or more policies for replacing the storage devices in a manner that ensures non-disruptive operation and replacement of the storage devices.

18. The method of claim 17 wherein implementing the one or more polices comprises implementing a policy that provides a gradual staged replacement of the storage devices over a predetermined time window.

19. The method of claim 17 wherein implementing the one or more polices comprises implementing a policy that enables replacement of one or two storage devices at a time on a storage array configured to sustain up to two storage device failures until data on the failed storage devices is reconstructed on replacement storage devices.

20. A non-transitory computer readable medium including program instructions for execution on one or more processors, the program instructions configured to:

implement a storage input/output (I/O) stack configured to predict failure of the storage devices and establish threshold conditions for replacing the storage devices;

periodically monitor results of soft and hard failures of the storage devices;

perform a heuristic procedure that combines the monitored results to calculate a predicted failure; and

recommend replacement of the storage devices using the threshold conditions based on current usage and failure patterns of the storage devices.