Patent Publication Number: US-9852073-B2

Title: System and method for data redundancy within a cache

Description:
TECHNICAL FIELD 
     This disclosure generally relates to a network with distributed shared memory. 
     BACKGROUND 
     As the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option available to these users is an information handling system. An information handling system generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information. Because technology and information handling needs and requirements vary between different users or applications, information handling systems may vary with respect to the type of information handled; the methods for handling the information; the methods for processing, storing or communicating the information; the amount of information processed, stored, or communicated; and the speed and efficiency with which the information is processed, stored, or communicated. The variations in information handling systems allow for information handling systems to be general or configured for a specific user or specific use such as financial transaction processing, airline reservations, enterprise data storage, or global communications. In addition, information handling systems may include or comprise a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems, data storage systems, and networking systems. 
     The information handling system may include one or more operating systems. An operating system serves many functions, such as controlling access to hardware resources and controlling the execution of application software. Operating systems also provide resources and services to support application software. These resources and services may include a file system, a centralized configuration database (such as the registry found in Microsoft Windows operating systems), a directory service, a graphical user interface, a networking stack, device drivers, and device management software. In some instances, services may be provided by other application software running on the information handling system, such as a database server. 
     Some information handling systems are designed to interact with other information handling systems over a computer network connection. In particular, certain information handling systems may be designed to monitor, configure, and adjust the features, functionality, and software of other information handling systems by communicating with those information handling systems over a network connection. For example, one information handling system might be configured to manage a shared, distributed cache. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  schematically depicts an example network with distributed shared memory. 
         FIG. 2  schematically depicts an example cache manager. 
         FIG. 3  schematically depicts another example cache manager. 
         FIG. 4  schematically depicts an example distributed shared memory environment with a clustered memory resource distributed across multiple network segments. 
         FIG. 5  depicts an example method for using a distributed shared memory resource. 
         FIGS. 6 and 7  schematically depict example communication stack configurations that may be employed to enable devices to access a distributed shared memory resource. 
     
    
    
     DESCRIPTION OF EXAMPLE EMBODIMENTS 
       FIG. 1  depicts an example computer network  20  with distributed memory. The memory resource and supporting systems may be configured in a variety of different ways and for different applications. Caching is one example of a use of computer network  20 . Accordingly, the distributed memory resource in the example of  FIG. 1 , and in other examples discussed herein, includes a clustered memory cache  22 . Referring specifically to  FIG. 1 , clustered memory cache  22  may be aggregated from and comprised of physical memory locations  24  on a plurality of physically distinct computing systems  26  (individually designated as Computing System  1 , Computing System  2 , etc.) and associated local cache managers  34  (individually designated as CM 1 , CM 2 , etc.). In particular embodiments, physical memory  24  may include one or more solid state devices (SSDs) including, for example, one or more SSDs compliant with a standard such as the Peripheral Component Interconnect Express (PCIe) standard. Physical memory  24  may include persistent or non-volatile memory devices  24  including, for example, flash and magnetic disk. In particular embodiments, each type of physical memory  24  (e.g., RAM, flash, magnetic disk) on a computing system  26  may have its own local cache manager  34 . Additionally, physical memory  24  may have hot plug capabilities, such that physical memory  24  may be inserted into, removed from, or swapped between computing systems  26  without the need for pausing the operation of computer network  20  or clustered cache  22 . Computer network  20  also includes a metadata service  30 , a plurality of clients  32  (only one of which is shown in the example embodiment of  FIG. 1 ), and, as described above, a plurality of local cache managers  34  (individually designated as CM 1 , CM 2 , etc.). In particular embodiments, metadata service  30  may be located on one or more computing systems  26 . Each of the local cache managers  34  is local to and associated with a different portion of clustered memory cache  22 . The metadata service, clients and local cache managers are all operatively coupled with each other via network  40 . In addition, one or more configuration managers  42  (only one is shown in the example of  FIG. 1 ), a policy manager  44 , and an admin interface  46  may also be provided as part of computer network  20  (and may, in particular embodiments, be operatively coupled to other elements via network  40 ), to provide various functions that will be described below. In particular embodiments, configuration manager  42  may be located on one or more computing systems  26 . Computer network  20  includes an auxiliary store  50  which may also be coupled to other elements in computer network  20  via network  40 . Auxiliary store  50  may include one or more storage devices or systems at various locations (local or remote), including but not limited to hard disks, file servers, disk arrays, storage area networks, and the like. Auxiliary store  50  may, in particular embodiments, include DAS backing devices (used by a particular computing system  26 ), SAN backing devices (shared among computing systems  26 ), or a combination of the two. 
     Clustered memory cache  22  provides a shared memory resource that can be accessed and used by the clients. Depending on the mode of operation, clients  32  can read from and write to the clustered memory cache and cause insertion and/or eviction of data items to/from the cache. 
     As used herein, “client” may broadly to refer to any hardware or software entity that makes use of the shared memory resource. For example, clients may include personal computers, workstations, servers and/or applications or other software running on such devices. 
     “Client” may also more specifically refer to a driver or other software entity that facilitates access to the shared memory resource. For example, as will be described in more detail, a driver can be loaded into memory of a networked computer, allowing applications and the operating system of that computer to recognize or make use of the clustered cache. 
     The distributed shared memory described herein may be operated in a variety of modes. Many of the examples discussed herein will refer to a mode where clustered memory cache  22  provides caching functionality for data used by clients  32 . In particular, data items read from an auxiliary store  50  may be cached in clustered memory cache  22 , and data items to be written to auxiliary store  50  may also be cached in clustered memory cache  22 . Thus, even though a particular client may have ready access to the auxiliary store (e.g., access to a file system stored on a hard disk), it may be desirable to place requested data in the clustered memory cache, so as to provide faster access to the data. 
     Local Cache Managers 
     Regardless of the particular mode of operation, the clustered memory cache may span multiple physically distinct computing systems. For example, in  FIG. 1 , clustered memory cache  22  includes memory from N different computing systems  26  (Computing System  1 , Computing System  2 , etc., through Computing System N). The individual computing systems can be of varying configurations, for example ranging from relatively low-powered personal devices to workstations to high-performance servers. SMP or other multiprocessor architectures may be employed as well, in which one or more of the computing systems employ multiple processors or cores interconnected via a multiprocessor bus or other interconnect. As described in detail herein, physical memory  24  from these physically distinct systems  26  may be aggregated via network  40  and made available to clients  32  as a unified logical resource. 
     Referring particularly to local cache managers  34 , each cache manager may be local to and associated with a different portion of clustered memory cache  22 . The cache managers typically are independent of one another, and each is configured to allocate and manage individual units of physical memory in its associated portion of clustered memory cache  22 . 
     The local cache managers can be configured to manage client references and access to cached data items. As an illustration, assume a particular client  32  needs access to a data item cached in the portion of clustered cache  22  that is managed by cache manager CM 1 . The client may query metadata service  30  to identify which local cache manager  34  (in this case, CM 1 ) manages the desired cached data item, as described in further detail below. Once the client knows the memory location for the cached item is managed by CM 1 , the client contacts CM 1  via network  40  to gain access to the cached item. If access is permitted, the cache manager CM 1  grants access and maintains a record of the fact that the requesting client has a reference to the memory location. The record may indicate, for example, that the client has a read lock on a particular block of memory that is managed by cache manager CM 1 . 
     In some embodiments, clustered cache  22  may be implemented using Remote Direct Memory Access (RDMA). RDMA implementations that may be employed include, but are not limited to, the Virtual Interface Architecture, InfiniBand, RDMA over Converged Ethernet (RoCE), RDMA over TCP/IP, and iWARP. In such a setting, the local cache manager may be configured to provide RDMA keys to requesting clients or otherwise manage the respective access controls of the RDMA implementation. 
     For any given cache manager, the associated portion of the clustered cache will often include many different blocks or other units of memory. In particular, referring to  FIG. 2 , an exemplary cache manager  34  is depicted, including a cache store  60 . In the depicted example, cache store  60  is schematically represented as a table, with a record (row entry) for each block or other unit of physical memory managed by the cache manager. In particular embodiments of clustered cache  22  having cache data replication functionality, one cache store  60  may be created in cache manager  34  for non-replica portions of clustered cache  22  managed by memory manger  34 . Separate cache stores  60  may be created in cache manager  34  for each replica store managed by memory manger  34 . The first column in the example is simply an index, tag or other identifier used to designate a particular block of memory. 
     The remaining column or columns may contain metadata or other information associated with the corresponding unit of memory and/or the data stored in that unit of memory. As depicted in  FIG. 2 , cache manager  34  may also include a monitor thread  62  to facilitate the acquisition and updating of the cache store information. The associated information may include, by way of example, information about read locks, write locks and/or other client references to the unit of memory; a filename/path hash or other mechanism for identifying the cached data item(s); status indicators; rates of eviction and insertion; temporal information such as time resident in the cache, time since last access, etc.; block size or other capacity information relating to the unit of memory; and/or other information concerning the memory unit, such as statistical information regarding usage of the memory unit or the items cached in the memory unit. These are but illustrative examples. Also, it should be understood that while cache store  60  is depicted schematically to include the information in a table, a variety of other data structures or mechanisms may be employed to maintain the information store. 
     Local cache managers  34  may also be configured to receive and respond to requests to insert particular data items into clustered cache  22 . As will be explained in more detail below, these cache insertion requests can arise from and be initiated by actions of metadata service  30  and clients  32 . In some cases, the local cache manager may deny the cache insertion request. One situation where an insertion request can be denied is if the request is directed to a block containing an item that cannot be immediately evicted, for example because there are active client references to the cached item. 
     Assuming, however, that the insertion request is grantable by the local cache manager, the local cache manager acknowledges and grants the request. The cache manager also coordinates the population of the respective memory block with the data item to be cached, and appropriately updates any associated information for the block in the cache store (e.g., cache store  60 ). 
     Similarly, each local cache manager  34  is configured to receive and respond to requests to evict items from its associated portion of clustered cache  22 . As with insertion requests, the eviction requests can arise from actions of the metadata service  30  and one or more of clients  32 , as will be explained in more detail below. Assuming the request is grantable, the cache manager acknowledges and grants the request, and flushes the memory block or takes other appropriate action to make the memory block available for caching of another item. 
     In some example embodiments, it will be desirable to notify clients  32  when items are to be evicted from the clustered cache. Accordingly, the local cache managers may also be configured to maintain back references to clients accessing items in the cache. For example, assume a client requests access to an item in a portion of the cache managed by a cache manager, and that the cache manager has responded by granting a read lock to the client. Having maintained a back reference to the client (e.g., in cache store  60 ), the local cache manager can then notify the client in the event of a pending eviction and request that the client release the lock. 
     As discussed above, each local cache manager may be local to and associated with a different portion of the clustered cache. Although cache managers may be referred to herein as “local” cache managers, they need not be physically proximate to the physical memory. The local cache managers may be located elsewhere in some embodiments. In the example of  FIG. 1 , each of the distinct computing systems  26  has an individual cache manager responsible for the physical memory  24  contributed by the computing system  26  to the clustered cache. Alternatively, multiple local cache managers may be employed within a computing system. 
     In particular embodiments, clustered memory cache  22  may operate in a write-through mode; that is, write operations (initiated, for example, by client  32 ) are not completed until data that has been written to cache  22  is also flushed to a backing store such as auxiliary store  50 . In other embodiments, clustered memory cache  22  may operate in a write-back mode; that is, write operations (initiated, for example, by client  32 ) are completed as soon as the data is written to cache  22 , and write data is flushed to a backing store such as auxiliary store  50  at a later time. This later time may occur, for example, when a client  32  issues a flush on all cache blocks to which it has written. 
     In particular embodiments, clustered cache  22  may include cache data replication functionality, described in further detail below. In an embodiment including the cache data replication functionality, physical memory  24  may include data representing a portion of clustered cache  22  as well as one or more replica stores of data representing another portion or portions of clustered cache  22 , with both the data and the replica stores managed by local cache manager  34 . As an example, with reference to  FIG. 1 , computing system  1  includes local cache manager CM 1 . The physical memory  24  associated with CM 1  may include both data representing a portion of clustered memory cache  22 , as well as a replica store of data representing the portion of clustered cache  22  associated with local cache manager CM 2 . Additionally, in an embodiment with cache data replication functionality, each unit of physical memory  24  may include certain metadata including, for example, memory  24  identifier (e.g., manufacture ID, worldwide name, etc.); for each replica store hosted by memory  24 , the identifier, state, and primary store; for each replica store replicating data in memory  24 , the replica store identifier and host memory  24 ; and for each cache block in memory  24 , whether the cache block is dirty/unflushed or clean (and if dirty, when the cache block became dirty), and if dirty/unflushed, the replica stores where this block is replicated. 
       FIG. 3  depicts an example of an alternate cache manager configuration. As in the previous example, computing system  70  is one of several physically distinct computing systems contributing physical memory  24  to a distributed memory resource. The example of  FIG. 3  illustrates two configuration variations that may be applied to any of the examples discussed herein. First, the figure demonstrates a configuration in which the memory contributed from a single computing system is allocated in to multiple different segments. The individual segments, which may or may not be contiguous, are each managed by a different cache manager  34  (individually and respectively designated as CMa, CMb and CMc). As described below, the use of multiple cache managers and memory segments on a single computing system may be used to allow exportation of physical memory to multiple different aggregate memory resources. On the other hand, it may be desirable to employ multiple cache managers even where the memory is contributed to a single cache cluster or other shared memory resource. 
     Secondly, the figure demonstrates the use of multiple different clusters. Specifically, each local cache manager and memory segment pairing in the  FIG. 3  example belongs to a different cache cluster (i.e., clusters  22   a ,  22   b  and  22   c ). Multiple cluster configurations may be employed for a variety of reasons, such as for security reasons, access control, and to designate specific clusters as being usable only by specific applications. 
     Local cache managers  34  may also be configured to report out information associated with the respective portions of clustered cache  22 . As discussed above with reference to  FIG. 2 , each cache manager may include a cache store  60  with information about the cache manager&#39;s memory locations. This information may be provided from time to time to metadata service  30 , configuration manager  42 , and/or other components of the systems described herein. 
     In particular embodiments, local cache manager may examine all possible local memory  24  devices upon startup or upon a plug-and-play event (indicating that memory  24  has been added to the associated computing system  26 ) to determine which memory  24  belongs to clustered cache  22 . This may, in particular embodiments, be determined by examining the memory identifier in the metadata of memory  24 . If it is determined that memory  24  belongs to clustered cache  22 , local cache manager  34  may update entries in its cache store  60  and communicate data regarding memory  24  to metadata service  30  or configuration manager  42  (including, for example, the journal in configuration manager  42 ). The determination whether memory  24  belongs to clustered cache  22  may, in some embodiments, be determined by examining an entry in the journal of configuration manager  42 . In particular embodiments, local cache manager  34  may not allow access to the newly-added memory  24  until the memory  24  has been approved by the configuration manager  42  (e.g., approved as not being obsolete after an examination of an entry in the journal of the configuration manager). 
     Metadata Service Data Store 
     For example, as will be described in more detail below, metadata service  30  can provide a centralized, or relatively centralized, location for maintaining status information about the clustered cache. In particular, in  FIG. 1 , cache managers CM 1 , CM 2 , etc. through CMN may be considered to all be within a domain that is assigned to metadata service  30 . Metadata service  30  can monitor the domain, for example by maintaining information similar to that described with reference to cache store  60 , but for all of the cache managers in the domain. 
     More particularly, metadata service  30  may include a metadata service data store  80  for maintaining information associated with the memory locations in its domain that form the clustered cache. In one class of examples, and as shown in  FIG. 1 , metadata service data store  80  may include multiple records  82 . Specifically, a record  82  is provided for each of the physical memory units  24  of clustered cache  22 . For example, assume clustered cache  22  includes 64 million 8-kilobyte memory blocks (512 gigabytes of addressable cache memory) spread across computing systems  1  through N and local cache managers CM 1  through CMN. In this example, metadata service data store  80  could be configured with 64 million records (rows), with each pertaining to one of the cache memory blocks in the cluster. In an alternate example, each record could apply to a grouping of memory locations. Numerous other arrangements are possible. 
     Various additional information may be associated with the records of metadata service data store  80 . In particular, the metadata service may store a tag for each of the memory locations of the cache, as shown in the figure. In one example, the tag allows a requesting entity, such as one of clients  32 , to readily determine whether a particular data item is stored in the cache. Specifically, the tag column entries may each be a hash of the path/filename for the data item resident in the associated memory block. To determine whether a requested data item (e.g., a file) is present in the cache, the path/filename of the requested item may be hashed using the same hash routine and the resulting hash compared to the tag column entries of the metadata service data store  80 . The path and filename hash described above is provided by way of example; hash methodologies may be employed on other data, and/or other identification schemes may be employed. 
     Metadata service data store  80  may also indicate an associated local cache manager for each of its records, as shown at the exemplary column designated “CM.” For example, data store  80  could indicate that a first memory block or range of memory blocks was managed by cache manager CM 1 , while a second bock or range of blocks was managed by local cache manager CM 2 . With such a designation, in the event that a query for a particular item reveals the item is present in the cache (e.g., via a match of the path/filename hash described above), then the response to that query can also indicate which local cache manager  34  should be dealt with to read or otherwise access the cached item. 
     In the example of  FIG. 1 , data store  80  also includes a status indication for each of the cache blocks. In one example, each of the cache blocks is indicated as having one of the following statuses: (1) empty, and therefore available to be populated; (2) insertion pending, indicating that the memory block is in the process of being populated with a newly-inserted cached item; (3) active, indicating that the memory block presently contains an active cached data item; or (4) deletion pending, indicating that the data item in the cache block is being deleted. It will be appreciated that these are illustrative examples, and other status information and flags may be employed. The specific exemplary status indications referred to above will be described in further detail below. 
     The tag, cache manager and status entries described above with reference to the cache blocks in data store  80  are non-limiting examples. As described in more detail below, metadata service  30  and its policy engine  90  typically play a role in implementing various policies relating to the configuration and usage of clustered cache  22 . Application of various policies can be dependent upon rates of eviction and insertion for a cache block or data item; temporal information such as the time a data item has been cached in a particular block, time since last access, etc.; and/or other information concerning the cache block, such as statistical information regarding usage of the cache block or the data items cached therein. 
     It will thus be appreciated that the information maintained in metadata service data store  80  may overlap to some extent with the information from the various cache stores  60  ( FIG. 2 ) of the local cache managers. Indeed, as previously indicated, the described system can be configured so that the cache managers provide periodic updates to maintain the information in the metadata service data store  80 . 
     Also, the metadata service may be distributed to some extent across the network infrastructure. For example, multiple mirrored copies of the metadata service may be employed, with each being assigned to a subset of local cache managers. Cache manager assignments could be dynamically reconfigured to achieve load balancing and in the event of failure or other changes in operating conditions of the environment. 
     Operational Examples—Cache Hit, Cache Miss 
     Various examples will now be described illustrating how clients  32  interact with metadata service  30  and local cache managers  34  to access clustered cache  22 . The basic context of these examples is as follows: a particular client  32  ( FIG. 1 ) is running on an applications server executing a data-intensive financial analysis and modeling program. To run a particular analysis, the program may need to access various large data files residing on auxiliary store  50 . 
     In a first example, the financial analysis program makes an attempt to access a data file that has already been written into clustered memory cache  22 . This may have occurred, for example, as a result of another user causing the file to be loaded into the cache. In this example, client  32  acts as a driver that provides the analysis program with access to the clustered memory cache  22 . Other example embodiments include client  32  operating in user mode, for example as an API for interacting with the clustered resource. 
     In response to the client request for the data file, metadata service  30  determines that the requested file is in fact present in the cache. This determination can be performed, for example, using the previously-described filename/path hash method. Metadata service  30  then responds to the request by providing client with certain metadata that will enable the client to look to the appropriate portion of the clustered memory cache (i.e., the portion containing the requested file). 
     In particular, metadata service  30  responds to the request by identifying the particular local cache manager  34  which is associated with the portion of the cache containing the requested file. This identification may include the network address of the local cache manager, a logical block address or a cache block number, or another identifier allowing derivation of the address. Once the client has this information, the client proceeds to negotiate with the local cache manager to access and read the requested file from the relevant block or blocks managed by the cache manager. This negotiation may include granting of a read lock or other reference from the local cache manager to the client, and/or provision of RDMA keys as described above. 
     As shown in  FIG. 1 , client  32  may include a local store  92  of metadata. In the above example, this local store may be used by the client to record the association between the requested data file and the corresponding local cache manager and respective portion of the clustered cache. Thus, by consulting local store  92 , subsequent cache accesses to the cached file can bypass the step of querying metadata service  30 . Indeed, clients  32  may be implemented to first consult local store  92  before querying metadata service  30 , thereby allowing clients to more directly and efficiently access cached items. Metadata service  30  may thus function in one respect as a directory for the clustered cache  22 . Clients having up-to-date knowledge of specific entries in the directory can bypass the directory and go directly to the relevant local cache manager. 
     In particular embodiments, local store  92  may include metadata such as a list of client write or read references to portions of clustered cache  22 . As an example, client  32  may keep track of which cache blocks it holds write references to (as well as which local cache manager  34  manages these cache blocks) in local store  92 . By keeping track of these write references, client  32  may be able to communicate with the corresponding local cache managers  34  and, upon request by a local memory manger  34 , release certain of its write references to allow the local cache manager  34  to make room in its corresponding memory  24  for new data to be cached. Local store  92  may also contain a queue of which cache blocks are most- or least-recently used by client  32 . Thus, if a particular cache block is the least recently used cache block by client  32 , then it will be at the front of the least-recently-used (LRU) queue in local store  92  and may be the first write reference that client  32  releases, either voluntarily or when asked by a local cache manager  34 . If there is a pending input/output request on a particular cache block, then the reference to that cache block may move to the back of the least-recently-used (LRU) queue in local store  92 . In particular embodiments, there may be a limit on the number of cache block references (write, read, or some combination of both) that a client  32  is allowed to have in using clustered cache  22 . This limit may be tracked, for example, by metadata service  30  (e.g., the policy engine  90 ), by one or more local memory mangers  34  (described below), or may be tracked and enforced at client  32  itself. 
     Another example will now be considered, in which the file requested by the analysis program is not present in clustered cache  22 . As before, the analysis program and/or client  32  cause the file request to issue, and the request is eventually received at metadata service  30 . Prior to messaging of the request to metadata service  30 , however, the local client store  92  of metadata is consulted. In this case, because the requested file is not present in the cache, no valid metadata will be present in the local store. The request is thus forward to metadata service  30 . 
     In response to the request, metadata service  30  cannot respond with a cache manager identification, as in the previous example, because the requested file is not present in the clustered cache. Accordingly, the hash matching operation, if applied to metadata service data store  80 , will not yield a match. 
     The metadata service can be configured to implement system policies in response to this type of cache miss situation. Specifically, policies may be implemented governing whether the requested item will be inserted into the clustered cache, and/or at what location in the cache the item will be written. Assuming clustered cache  22  is populated with the requested item, the metadata service data store  80  will be updated with metadata including the designation of the responsible cache manager  34 . This metadata can then be supplied in response to the original request and any subsequent requests for the item, so that the cached version can be accessed through client interactions with the appropriate cache manager. 
     Policies 
     The systems and methods described herein may be configured with various policies pertaining to the shared memory resource. Policies may control configuration and usage of the clustered memory cache; client access to the cache; insertion and eviction of items to and from the cache; caching of items in particular locations; movement of cached items from one location to another within the cache; etc. Policies may also govern start/stop events, such as how to handle failure or termination of one of the computing systems contributing memory locations to the cluster. These are non-limiting examples—a wide variety of possibilities exist. 
     In the example of  FIG. 1 , configuration manager  42 , admin interface  46  and policy manager  44  perform various functions in connection with the policies. In particular, admin interface  46  can provide a command-line, graphical or other interface that can be used by a system administrator to define policies and control how they are applied. Configuration manager  42  typically is adapted to coordinate startup events, such as the login or registration of entities as they come on-line. In many settings, startup procedures will also include distribution of policies. 
     For example, in  FIG. 1 , initialization of clients  32  is handled by configuration manager  42 . Specifically, when coming on-line, each client  32  initializes and registers with configuration manager  42 . Configuration manager  42  provides the initializing client with addresses of the appropriate metadata service  30 . Configuration manager  42  may also retrieve relevant policies from policy manager  44  and distribute them to the client, which stores them locally for implementation via client policy engine  94  ( FIG. 1 ). 
     Configuration manager  42  typically also coordinates registration and policy distributions for metadata service  30  and local cache managers  34 . The distributed policies may be stored locally and implemented via metadata service policy engine  90  ( FIG. 1 ) and cache manager policy engines  64  ( FIG. 2 ), respectively. From time to time during operation, the size and underlying makeup of the clustered memory resource may change as local cache managers launch and terminate, either intentionally or as a result of a failure or other unintentional system change. These startups and terminations may be handled by the configuration manager, to provide for dynamic changes in the shared memory resource. For example, during periods where heavier usage volume is detected (e.g., an escalation in the number of cache insertion requests), the configuration manager may coordinate with various distributed devices and their associated cache managers to dynamically scale up the resource. On the other hand, performance lags or other circumstances may dictate a dynamic adjustment where one or more cache managers are taken off-line. As described in more detail below, the present system may be configured to permit migration of cache data from one location to another in the shared resource. The startups and terminations described above provide examples of situations where such data migration may be desirable. 
     In particular embodiments, configuration manager  42  may include a journal (or any suitable data structure) containing state information about clustered cache  22 , stored locally in persistent or non-volatile memory. Because the journal is maintained in persistent memory in configuration manager  42 , even if the configuration manager fails (or, in the case of multiple configuration managers, if any or all of the configuration managers  42  of network  20  fail), cache state information may still be maintained. In particular embodiments, the journal may be mirrored elsewhere in network  20  or in clustered memory cache  22 . Even in the case of a complete failure of all copies of the journal, the journal may be reconstructed from metadata information stored in memory  24  (described above); if memory  24  is non-volatile memory, then the journal may be reconstructed even after a complete shutdown of cache  22 . 
     The journal of the configuration manager  42  may include the following information about each memory unit  24  of the clustered cache  22 : one or more memory  24  identifiers (e.g., manufacture ID, worldwide name, cache-specific name, etc.), memory  24  type (e.g., RAM, flash, persistent local disk), memory  24  size, memory  24  state (e.g., inactive, active, failed, failed and recovered, removed), an identifier of the local cache manager  34  that manages memory  24  (e.g., the local cache manager that most recently registered memory  24  with the journal), associated replica store identifiers (e.g., physical IDs of memory  24  containing any associated replica stores, cache-specific IDs of memory  24  containing replica stores), an identifier of the local cache manager(s)  34  of the associated replica store(s), associated replica store states, and replica stores that are currently being re-hosted on associated replica stores. Additionally, the journal may also include information about the one or more metadata services  30  that are part of the clustered cache  22  including, for example, the identifiers of any metadata servers that have been expelled from cache  22 . The journal may also include partition map generation numbers, local cache manager  34  membership generation numbers, and, for each auxiliary store  50  (or each device in auxiliary store  50 ), a device pathname and a device state. 
     The configuration manager  42  may communicate with metadata service  30  (including, for example, data store  80 ), clients  32 , local cache managers  34  (including, e.g., cache store  60 ), or any other part of network  20  to obtain information to update entries in its journal. Additionally, entries in the journal may be examined by configuration manager  42  to communicate information to metadata service  30  (including, for example, data store  80 ), clients  32 , local cache managers  34  (including, e.g., cache store  60 ), or any other part of network  20 . 
     As an example, if a local cache manager  34  communicates to configuration manager  42  that a new physical memory  24  has been detected (e.g., upon startup or upon a plug-and-play event) and also communicates the memory identifier in the metadata of new memory  24 , the configuration manager  42  may examine its journal to determine whether the memory identifier corresponds to an existing memory unit in cache  22  or whether a new entry must be created for the new memory  24 . Additionally, the configuration manager may also determine, if the identifier corresponds to an existing memory unit in cache  22 , whether the existing memory unit is valid for use (e.g., based on the memory state—whether failed, recovered, removed, etc.). Configuration manager  42  may then communicate to local cache manager whether the “new” memory  24  may be used by local cache manager  34 . If so, local cache manager  34  may update entries in its cache store  60  and communicate data regarding memory  24  to metadata service  30  or configuration manager  42 . 
     As another example, a local cache manager  34  may report the failure of a unit of memory  24 . Configuration manager  42  may update its journal to record the new state of the memory  24 , and may examine its journal to determine whether a replica store exist for memory  24 , and if so, which local cache manager manages this replica store. Configuration manager  42  may communicate with the local memory manger managing the replica store and tell it to “absorb” the replica as a normal (non-replica) portion of the cache  22 , and subsequently the journal may be updated. Configuration manager  42  may also communicate with yet another local cache manager to create a new replica store for the absorbed replicas (e.g., in the same physical memory  24  containing replica stores for the local cache manager who has “absorbed” the replica), and subsequently update the journal. 
     As indicated above, policy manager  44  may be configured to provide a master/central store for the system policy definitions, some or all of which may be derived from inputs received via admin interface  46 . Policy manager  44  may also validate or verify aggregate policies to ensure that they are valid and to check for and resolve policy conflicts. The policy manager  44  typically also plays a role in gathering statistics relating to policy implementations. For example, the policy manager may track the number of policy hits (the number of times particular policies are triggered), and/or the frequency of hits, in order to monitor the policy regime, provide feedback to the admin interface, and make appropriate adjustments. For example, removal of unused policies may reduce the processing overhead used to run the policy regime. 
     As should be appreciated from the foregoing, although the policies may be defined and managed centrally, they may also be distributed and implemented at various locations in the system. Furthermore, the policy ruleset in force at any given location in the system may vary based on the nature of that location. For example, relative to any one of cache managers  34  or clients  32 , metadata service  30  has a more system-wide global view of clustered cache  22 . Accordingly, policy rulesets affecting multiple clients or cache managers can be distributed to and implemented at metadata service  30 . 
     Policy Examples—Client Filter 
     Referring to clients  32 , and more particularly to the client policy engines  94  incorporated into each client, various exemplary client-level policy implementations will be described. Many example policies implemented at the clients operate as filters to selectively control which client behaviors are permitted to impact the shared memory resource. More specifically, the client policy engine may be configured to control whether requests for data items (e.g., an application attempting to read a particular file from auxiliary store  50 ) are passed on to metadata service  30 , thereby potentially triggering an attempted cache insertion or other action affecting the clustered cache. 
     The selective blocking of client interactions with metadata service  30  operates effectively as a determination of whether a file or other data item is cacheable. This determination and the corresponding policy may be based on a wide variety of factors and criteria. Non-limiting examples include:
         (1) Size—i.e., items are determined as being cacheable by comparing the item size to a reference threshold. For example, files larger than N bytes are cacheable.   (2) Location—i.e., items are determined as being cacheable depending on the location of the item. For example, all files in a specified path or storage device are cacheable.   (3) Whitelist/Blacklist—a list of files or other items may be specifically designated as being cacheable or non-cacheable.   (4) Permission level or other flag/attribute—for example, only read-only files are cacheable.   (5) Application ID—i.e., the cacheable determination is made with respect to the identity of the application requesting the item. For example, specified applications may be denied or granted access to the cache.   (6) User ID—e.g., the client policy engine may be configured to make the cacheable determination based on the identity of the user responsible for the request.   (7) Time of Day.
 
In addition, these examples may be combined (e.g., via logical operators). Also, as indicated above, the list is illustrative only, and the cacheability determination may be made based on parameters other than the cited examples.
       

     Policy Examples—Cache Insertion and Cache Eviction 
     Cache insertion policies may determine whether or not a file or other data item may be inserted into clustered cache  22 . For example, cache insertion policies may be applied by metadata service  30  and its policy engine  90 , though application of a given policy may be based upon requests received from one or more clients  32 , and/or upon metadata updates and other messaging received from the local cache managers  34  and maintained in metadata service data store  80  ( FIG. 1 ). 
     In some examples, administrators or other users are able to set priorities for particular items, such as assigning relatively higher or lower priorities to particular files/paths. In addition, the insertion logic may also run as a service in conjunction with metadata service  30  to determine priorities at run time based on access patterns (e.g., file access patterns compiled from observation of client file requests). 
     Further non-limiting examples of cache insertion policies include:
         (1) Determining at metadata service  30  whether to insert a file into clustered memory cache  22  based on the number and/or frequency of requests received for the file. The metadata service can be configured to initiate an insertion when a threshold is exceeded.   (2) Determining at metadata service  30  whether to insert a file into clustered memory cache  22  based on available space in the cache. This determination typically will involve balancing of the size of the file with the free space in the cache and the additional space obtainable through cache evictions. Assessment of free and evictable space may be based on information in metadata service data store  80 .   (3) Determining at metadata service  30  whether to insert a file into clustered memory cache  22  based on relative priority of the file.       

     Metadata service  30  may also implement eviction policies for the clustered cache  22 . Eviction policies determine which data items to evict from the cache as the cache reaches capacity. Eviction policies may be user-configured (e.g., by an administrator using admin interface  46 ) based on the requirements of a given setting, and may be applied based on metadata and other information stored at metadata service  30  and/or cache managers  34 . 
     In particular, metadata service  30  may reference its data store  80  and predicate evictions based on which memory location within its domain has been least recently used (LRU) or least frequently used (LFU). Other possibilities include evicting the oldest record, or basing evictions on age and frequency based thresholds. These are provided as examples, and evictions may be based upon a wide variety of criteria in addition to or instead of these methods. 
     As previously mentioned, although metadata service  30  has a global view of the cache and is therefore well-positioned to make insertion/eviction determinations, the actual evictions and insertions are carried out by the cache managers  34  in some embodiments. Indeed, the insertion/eviction determinations made by metadata service  30  are often presented to the cache managers as requests that the cache managers can grant or deny. In other cases, the cache manager may grant the request, but only after performing other operations, such as forcing a client to release a block reference prior to eviction of the block. 
     In other cases, metadata service  30  may assign higher priority to insertion/eviction requests, essentially requiring that the requests be granted. For example, the overall policy configuration of the system may assign super-priority to certain files. Accordingly, when one of clients  32  requests a super-priority file, if necessary the metadata service  30  will command one or more cache managers  34  to evict other data items and perform the insertion. 
     In many embodiments, however, the local cache managers have authority over the cache memory locations that they manage, and are able in certain circumstances to decline requests from metadata service  30 . One reason for this is that the cache managers may have more accurate and/or current information about their associated portion of the cache. Information at the cache managers may be more granular, or the cache managers may maintain certain information that is not stored at or reported to metadata service  30 . On the other hand, there may be delays between changes occurring in the cache and the reporting of those changes from the respective cache manager to metadata service  30 . For example, metadata service  30  might show that a particular block is evictable, when in fact its cache manager had granted multiple read locks since the last update to the metadata service. Such information delays could result from conscious decisions regarding operation of the clustered cache system. For example, an administrator might want to limit the reporting schedule so as to control the amount of network traffic associated with managing the shared memory resource. 
     The above-described distribution of information, functionality and complexity can provide a number of advantages. The highly-distributed and non-blocking nature of many of the examples discussed herein may allow them to be readily scaled in large datacenter environments. The distributed locking and insertion/eviction authority carried out by the cache managers may allow for many concurrent operations and reduce the chance of any one thread blocking the shared resource. Also, the complicated tasks of actually accessing the cache blocks are distributed across the cluster. This distribution is balanced, however, by the relatively centralized metadata service  30 , and the global information and management functionality it provides. 
     Furthermore, it should be appreciated that various different persistence modes may be employed in connection with the clustered memory resource described herein. In many of the examples discussed herein, a read-only caching mode is described, where the clustered resource functions to store redundant copies of data items from an underlying auxiliary store. This may enhance performance because the cluster provides a shareable resource that is typically faster than the auxiliary store where the data originates. However, from a persistence standpoint, the data in the cluster may be flushed at any time without concern for data loss because the cluster does not serve as the primary data store. Alternatively, the cluster may be operated as a primary store, with clients being permitted to write to locations in the cluster in addition to performing read operations. In this persistence mode, the cluster data may be periodically written to a hard disk or other back-end storage device. 
     A further example of how the clustered memory resource may be used is as a secondary paging mechanism. Page swapping techniques employing hard disks are well known. The systems and methods described herein may be used to provide an alternate paging mechanism, where pages are swapped out the high performance memory cluster. 
     Policy Examples—Locality within Clustered Cache 
     The exemplary policy regimes described herein may also operate to control the location in clustered cache  22  where various caching operations are performed. In one class of examples, metadata service  30  selects a particular cache manager  34  or cache managers to handle insertion of a file or other item into the respective portion of the cache. This selection may be based on various criteria, and may also include spreading or striping an item across multiple portions of the cluster to provide increased security or protection against failures. 
     In another class of examples, the metadata service coordinates migration of cached items within clustered memory cache  22 , for example from one location to another in the cache. This migration may be necessary or desirable to achieve load balancing or other performance benefits. 
     A variety of exemplary locality policies will now be described, at times with reference to  FIG. 1  and  FIG. 4 .  FIG. 4  depicts another example of a shared-memory computer network  20 . The depicted example is similar in many respects to the example of  FIG. 1 , except that network  40  includes multiple segments. Two segments are depicted: Segment A and Segment B. The segments may be separated by a router, switch, etc. As before, clustered memory cache  22  is comprised of memory  24  from multiple physically distinct computing systems  26 , however some portions of the cache are local to network Segment A, while others are local to network Segment B. Clients  32   a , auxiliary store  50   a  and metadata service  30   a  are on Segment A, while Clients  32   b , auxiliary store  50   b  and metadata service  30   b  are on Segment A 
     In a first example, cache insertion locality is determined based on relative usage of memory locations  24 . Usage information may be gathered over time and maintained by cache managers  34  and the metadata services, and maintained in their respective stores. Usage may be based on or derived from eviction rates, insertion rates, access frequency, numbers of locks/references granted for particular blocks, etc. Accordingly, when determining where to insert an item in clustered cache  22 , the metadata service may select a less utilized or underutilized portion of the cache to achieve load balancing. 
     The metadata service may also coordinate migration of cache items from one location to another based on relative usage information. For example, if information in metadata service data store  80  ( FIG. 1 ) indicates unacceptable or burdensome over-usage at cache managers CM 2  and CM 3 , metadata service  30  can coordinate relocation of some of the data items to other cache managers (e.g., cache managers CM 1  or CM 4 ). 
     In another example, locality policies are implemented based on location of the requesting client. Assume for example, with reference to  FIG. 4 , that a cache insertion request is triggered based on an application associated with one of clients  32   a  (Segment A). The policy configuration could be implemented such that this would result in an attempted insertion at one of the Segment A cache managers (CM 1 , CM 2  or CM 3 ) instead of the Segment B managers. In yet another example, if a client  32   a  has an application that is located on a computing system  26  on Segment A, a policy configuration could be implemented such that this would result in an attempted insertion at the Segment A cache manager (CM 1 , CM 2  or CM 3 ) that is co-located on the same computing system  26  as the application. 
     In particular embodiments, a locality policy may be implemented based on the location of the client most likely to access the data. As an example, in the case of virtualization environments, it is often the case that a single virtual machine (a type of client application) accesses a cache block without overlapping or sharing this cache block with another client  32  or client application. Thus, as described above, one locality policy may be to locate the requested data from auxiliary store  50  in a cache block in the memory  24  of the same computing system  26  hosting the virtual machine application. Because it is unlikely (in the case of a virtual machine application) that a request for that same data would come from another client application, if a different cache manager  34  (or computing system  26 ) seeks to access this same data due to a client request, it is likely that the virtual machine application has actually migrated to a portion of network  20  associated with this different cache manager  34  (or computing system  26 ). Thus, in one implementation of this locality policy (whether for virtual machine applications or general client applications), a timer is started when a second cache manager (or computing system) seeks to access (at the request of a client application) the same data that is stored in a cache block co-located with a first client application and managed by a first cache manager that created (or allocated or wrote) the cache block. Metadata associated with the cache block (located, e.g., in cache store  60  or in memory  24  itself) may contain an identifier for the client or client application who initially requested the cache block. If a certain amount of time has passed (e.g., several seconds or several milliseconds) since the first cache manager or client application has accessed the cache block, it may be determined that the first client application has actually migrated to a second portion of network  20  associated with the second cache manager. The cache block may then be migrated to the second cache manager&#39;s associated memory in order to serve the client application in its new location. In particular embodiments, once a cache block has been migrated, a second timer is started, such that the cache block cannot be migrated (for locality policy reasons) again until the second timer reaches a predetermined value (e.g., one hour). The pattern of access to a particular cache block by client applications (or cache managers) may, in particular embodiments, be stored and tracked (e.g. in cache stores  60 ) before it is determined whether a migration of a client application has occurred and whether the cache block should also be migrated. Additionally, a variety of statistics regarding accesses to individual cache blocks or groups of associated or correlated cache blocks may also be tracked by cache managers  34  and stored in cache store  60 . The locality policy may be turned on or off depending on a variety of factors, and it may be applied globally within cache  22  or locally within certain segments of network  40 . For example, the policy may be turned on or off depending on whether a particular logical volume contains support for virtualized data. Additionally, certain clients may have more or less priority in terms of the locality policy than other clients. For example, even if a particular client application accesses a cache block frequently, if it is a low priority client application, it will not trigger a migration event for the cache block. In yet another embodiment, data relating to the performance of access times (collected, e.g., from clients  32 ) may be used to determine whether network  20  has slow or fast links, and to use this information in determining whether and where to migrate cache blocks within the network. Metadata relating to this locality policy (stored, e.g., in cache store  60  or in memory  24 ) may include bits indicating the type of placement policy, a time stamp for the last access to the cache block, and the network address (e.g., IP address) for the last accessor. Any or all of this data may be communicated to or stored in metadata service  30  (including data store  80 ) or configuration manager  42  (including a journal), and any locality policy may be controlled by metadata service  30 , configuration manager  42 , policy manager  44 , or any other suitable component of computer network  20 . 
     In another example, the relative location of the underlying data item is factored into the locality policy. Referring to  FIG. 4 , policies may be configured to specify that files located on auxiliary store  50   b  (on Segment B) are to be cached with the Segment B cache managers  34 . This may be the case even where the requesting client is located on Segment A. Where policy implementations compete, as in this example, other aspects of the policy configuration can resolve the conflict, for example through prioritization of various components of the overall policy regime. 
     From the above, it should be understood that locality may be determined by tracking usage patterns across the cluster and migrating memory blocks to nodes optimized to reduce the total number of network hops involved in current and anticipated uses of the cluster. In many cases, such optimization will significantly reduce latency and potential for network congestion. The usage data may be aggregated from the clients by the configuration manager and propagated to the metadata service(s) as a form of policy that prioritizes various cache blocks. 
     The policy implementation may also be employed to detect thrashing of data items. For example, upon detecting high rates of insertion and eviction for a particular data item, the system may adjust to relax eviction criteria or otherwise reduce the thrashing condition. 
     A further locality example includes embodiments in which a block or data item is replicated at numerous locations within the clustered memory resource, described further below. In certain settings, such replication will improve fault tolerance, performance, and may provide other advantages. For example, in a caching system, multiple copies of a given cache block could be sited at multiple different locations within the clustered cache. A metadata service query would then result in identification of one of the valid locations. In some embodiments, the second valid location may be maintained as a replica purely for fault tolerance purposes and may not be directly accessible to clients. 
     Examples Method—Flowchart— FIG. 5   
     Referring now to  FIG. 5 , an example shared memory method  120  will be described, in the context of client entities accessing a clustered memory cache. As before, the clustered memory cache may be aggregated from and comprised of physical memory on multiple physically distinct computing systems. The context further includes attempts by the clients to access data items that are stored in an auxiliary store, but which may also be inserted into the clustered memory cache. 
     The method may generally include running a local cache manager on each of a plurality of physically distinct computing systems operatively coupled with each other via network infrastructure. One or more metadata services are instantiated, and operatively coupled with the network infrastructure. Communications are conducted between the metadata service(s) and the local cache managers to provide the metadata service with metadata (e.g., file/path hashes, usage information/statistics, status, etc.) associated with the physical memory locations. The metadata service may then be operated to provide a directory service and otherwise coordinate the cache managers, such that the physical memory locations are collectively usable by clients as an undifferentiated memory resource. 
     Referring specifically to the figure, at  122 , method  120  may also include issuing of a client request. As in the examples described above, the request may originate or issue from an operating system component, application, driver, library or other client entity, and may be directed toward a file or other data item residing on a file server, disk array or other auxiliary store. 
     As shown at  124 , method  120  may also include checking a local store to determine whether metadata is already available for the requested item. The existence of local metadata indicates that the requested item is currently present and active in the clustered memory cache, or at least that it was at some time in the past. If local metadata is available, a read lock is obtained if necessary ( 126 ) and the item is read from its location in clustered memory cache ( 128 ). 
     In the context of  FIG. 1 , these steps could correspond to an application request, via client  32 , for a particular file located on auxiliary store  50 . In response to the request, client  32  would retrieve valid metadata for the requested file from local metadata store  92 . The retrieved metadata would indicate the particular cache manager  34  for the data item, and/or would otherwise indicate the location of the data item in clustered cache  22 . The requesting client would then access the item from its location in the cache, for example by interacting with the respective cache manager to obtain a read lock and perform an RDMA read of the cached item. 
     Continuing with  FIG. 5 , if it cannot be determined from the local store that the requested item is or had been cached in the shared memory resource, method  120  may include a determination of whether the item is eligible for caching, as shown at  130 . Referring again to  FIG. 1 , client  32  and its policy engine  94  provide examples of components configured to make the eligibility determination of step  130 . Specifically, as discussed above, the client and policy engine may filter the passing of requests to metadata service  30 , and thereby filter the usage of clustered memory cache. 
     If the requested item is not eligible for caching, the request is satisfied by means other than through the clustered memory cache. In particular, as shown at  132 , the client request is satisfied through auxiliary access, for example by directly accessing a back-end file system residing on auxiliary store  50  ( FIG. 1 ). 
     Proceeding to  134 , a metadata service may be accessed for eligible requests that cannot be initiated with locally stored metadata. Similar to the inquiry at step  124 , the metadata service is queried at  136  to determine whether metadata exists corresponding to the client request. If the metadata service has current metadata for the request (e.g., the address of a local cache manager overseeing a portion of cache  22  where the requested item is cached), then the metadata is returned to the requesting entity ( 138 ), and the access and read operations may proceed as described above with reference to steps  126  and  128 . 
     The absence of current metadata at the queried metadata service is an indication that the requested item is not present in the shared memory resource (e.g., clustered memory cache  22  of  FIG. 1  does not contain a non-stale copy of a file requested by one of clients  32 ). Accordingly, as shown at  140 , method  120  may include determining whether an attempt will be made to insert the requested item into the shared memory. If the item will not be inserted, the client request may be serviced other than through use of the shared resource, as previously described and shown at  132 . 
     Continuing with  FIG. 5 , if an insertion is to be made, method  120  may include determining the locality of the insertion, as shown at  142 . More particularly, an assessment may be made as to a specific location or locations within the shared memory resource where the item is to be placed. 
     As in the various examples discussed with reference to  FIG. 1 , the locality determination may be made based on various parameters and in accordance with system policy configurations. In some cases, locality will also be determined in response to data gathered during operation, for example usage statistics accumulated at a metadata service based on reports from cache managers. 
     As also shown at  142 , the cache insertion may also include messaging or otherwise conferring with one or more local cache managers (e.g., cache managers CM 1 , CM 2 , etc. of  FIG. 1 ). This communication may include requests, acknowledgments and the like. As an illustration, metadata service  30  might determine, based on usage statistics and certain metadata, to attempt to cache a requested block of data in a memory location managed by cache manager CM 4 . Metadata service  30  would send the insertion request to cache manager CM 4 , which could then grant the request and permitted the requested block to be written into its managed memory location  24 . The interaction of metadata service  30  and cache manager CM 4  can also include receiving an acknowledgment at the metadata service, as shown at  144 . 
     As previously discussed, the cache manager in some cases may deny the insertion request, or may honor the request only after performing an eviction or other operation on its managed memory location(s). Indeed, in some cases, insertion requests will be sent to different cache managers, successively or in parallel, before the appropriate insertion location is determined. In any event, the insertion process will typically also include updating the metadata service data store, as also shown at  144 . For example, in the case of a cached file, the data store  80  of metadata service  30  ( FIG. 1 ) may be updated with a hash of the path/filename for the file. 
     As shown at  146 , if the insertion is successful, metadata may be provided to the client and the access and read operations can then proceed ( 138 ,  126 ,  128 ). On the other hand, failed insertion attempts may result in further attempts ( 142 ,  144 ) and/or in auxiliary access of the requested item ( 132 ).
         Client Configuration—Libraries, Drivers, Virtual Memory, Page Fault Handling       

     Referring now to  FIGS. 6 and 7 , the figures depict exemplary architectures that may be employed to provide clients  32  with access to the shared memory resource(s). The figures depict various components of client  32  in terms of a communications stack for accessing data items, and show access pathways for reading data items from an auxiliary store (e.g., auxiliary store  50  of  FIG. 1 ) or from a clustered memory resource (e.g., clustered memory cache  22  of  FIG. 1 ), which typically provides faster and more efficient access than the auxiliary store access. 
     In the example of  FIG. 6 , cluster interface  602  is disposed in the communications stack between application  600  and file system abstraction layer  604 . Auxiliary store access may be made by the file system layer through known mechanisms such as TCP/IP—Ethernet layers  606 , SCSI—Fibre Channel layers  608 , and the like. As discussed above, auxiliary store access may occur for a variety of reasons. The file requested by application  600  might be of a type that is not eligible for loading into clustered memory cache. Cluster interface  602  may apply a filter that blocks or prevents access to the shared memory resource, as in step  130  of the exemplary method of  FIG. 5 . Alternatively, auxiliary store access may be performed after a failed cluster insertion attempt, as shown at steps  146  and  132  of  FIG. 5 . 
     Alternatively, cluster interface  602  is configured to bypass file system layer  604  in some cases and read the requested data from a location in the shared memory resource (e.g., a memory location  24  in clustered memory cache  22 ), instead of from the auxiliary store  50 . As indicated, this access of the clustered resource may occur via a client RDMA (over Infiniband/iWarp/RoCE) layer  610  and a target host channel adapter  612 . 
     Cluster interface  602  may perform various functions in connection with the access of the shared memory resource. For example, interface  602  may search for and retrieve metadata in response to a request for a particular file by application  600  (e.g., as in step  124  or steps  134 ,  136  and  138  of  FIG. 5 ). Interface  602  may also interact with a metadata service to insert a file into the clustered cache, and then, upon successful insertion, retrieve metadata for the file to allow the cluster interface  602  to read the file from the appropriate location in the clustered cache. 
     In one example embodiment, cluster interface  602  interacts with the virtual memory system of the client device, and employs a page-fault mechanism. Specifically, when a requested item is not present in the local memory of the client device, a virtual memory page fault is generated. Responsive to the issuance of the page fault, cluster interface  602  performs the previously described processing to obtain the requested item from the auxiliary store  50  or the shared memory cluster. Cluster interface  602  may be configured so that, when use of the clustered cache  22  is permitted, item retrieval is attempted by the client simultaneously from auxiliary store  50  and clustered memory cache  22 . Alternatively, attempts to access the clustered cache  22  may occur first, with auxiliary access occurring only after a failure. 
       FIG. 7  alternatively depicts a block-based system, where cluster interface  602  is positioned between the file layer  604  and block-based access mechanisms, such as SCSI—Fibre Channel layer  608  and SRP  620 , ISER  622  and RDMA—Infiniband/iWarp (or RoCE) layers  610 . In this example, the mechanisms for storing and accessing blocks are consistent with the file-based example of  FIG. 6 , though the data blocks are referenced from the device with an offset and length instead of via the file path. In particular embodiments, application  600  may be a virtual machine. Additionally, cluster interface  602  may be part of a virtual appliance with which a virtual machine communicates. In particular embodiments, a combination of iSER and RDMA transports may be used (in conjunction with iSER target devices in the virtual machine). In yet other embodiments, a native driver (operable to function with cache cluster  22 ) may be placed inside a hypervisor itself, and may use the RDMA stack instead of iSER in its data path. In these example embodiments, I/O flows from a virtual machine file system (e.g.,  604 ) to a native driver and then to a local cache manager  34 , for example, running inside a virtual machine. 
     Depending on the particular configuration employed at the client, block-level or file-level invalidation may be employed. For example, in the event that an application is writing to a data item that is cached in the clustered resource, the cached copy is invalidated, and an eviction may be carried out at the local memory/cache manager in the cluster where the item was stored. Along with the eviction, messaging may be sent to clients holding references to the cached item notifying them of the eviction. Depending on the system configuration, the clients may then perform block or file-level invalidation. 
     Furthermore, it will be appreciated that variable block sizes may be employed in block-based implementations. Specifically, block sizes may be determined in accordance with policy specifications. It is contemplated that block size may have a significant affect on performance in certain settings. 
     Finally, configurations may be employed using APIs or other mechanisms that are not file or block-based. 
     Policy Example—Cache Data Replication 
     In particular embodiments, clustered cache  22  may include cache data replication functionality. This cache data replication functionality may be managed by configuration manager  42 , metadata service  30 , local cache managers  34 , or any combination of these elements of network  20 . In an embodiment including the cache data replication functionality, physical memory  24  may include data representing a portion of clustered cache  22  as well as one or more replica stores of data representing another portion or portions of clustered cache  22 , with both the data and the replica stores managed by local cache manager  34 . In particular embodiments, the replica stores of clustered cache  22  may not be directly accessible to client  32 . In such an embodiment, the replica stores may be used for improved fault tolerance. As an example, with reference to  FIG. 1 , computing system  1  includes local cache manager CM 1 . The physical memory  24  associated with and managed by CM 1  may include both data representing a portion of clustered cache  22 , as well as a replica store of data representing the portion of clustered cache  22  associated with local cache manager CM 2 . 
     This type of cache data replication functionality may prevent the loss of data written to clustered cache  22 . Such a loss may be caused by a failure between the time a write to the clustered cache  22  completes and the time this written data is flushed from the cache to a backing store, such as auxiliary store  50 . Types of failure may include, for example, failure of a portion of physical memory  24 , failure of a local cache manager  34 , or failure of a computing system. 
     In particular embodiments, physical memory  24  may include multiple cache blocks. Each of these cache blocks, in turn, may include multiple disk blocks; as an example (and without limitation), each cache block may include between 32 and 256 disk blocks. In particular embodiments, clustered cache  22  may replicate only “dirty” cache blocks (e.g., cache blocks with write data that has not yet been flushed to auxiliary store  50 ). Data replication of cache blocks (e.g., dirty cache blocks) within cache  22  may be accomplished generally by the following steps. First, when a write to cache  22  occurs, the write data is written to some unit of physical memory  24 , e.g. a cache block within memory  24 , managed by a local cache manager  34 . The write data is logically copied from its cache block to some number (one or more) of replica cache blocks in a different physical memory unit  24  managed by a different local cache manager  34 . Once the data is written both to its original destination cache block and to any and all replica cache blocks, the write is completed (e.g., completed back to client  32 ). In embodiments in which only “dirty” cache blocks are replicated, the write may be completed (e.g., back to client  32 ) before the data of the cache block is written to auxiliary store  50 , as long as replica cache blocks have been created and written. Thus, if a cache block (or larger portion of physical memory  24 ) later fails, the clustered cache  22  may switch to using the replica for the failed portion of cache  22  and resume operation. As described earlier, in particular embodiments, the replica cache blocks may not be accessible to a client  32  in the manner that the cache blocks may be accessible to the client. 
     In the example embodiment of each physical memory  24  having exactly one associated replica store, the replica store may be located in a different physical memory  24  (managed by a different local cache manager  34 ). Thus, in the example of  FIG. 1 , if physical memory  24  located on computing system  1  (and managed by CM 1 ) has exactly one replica store for its cache blocks, for example on physical memory  24  located on computing system  4  (and managed by CM 4 ), both the physical memory on computing system  1  and the physical memory on computing system  4  would have to fail or be inaccessible for the relevant cache blocks to become unavailable to clustered cache  22 . By placing the replica store in a different physical memory  24 , fault tolerance for the system may be increased. In particular embodiments, if physical memory  24  (managed, for example by CM 1 ) includes multiple distinct memory units, each unit having exactly one replica, the replicas of all of these memory units will be managed by a single local cache manager (for example, CM 4 ). In yet other embodiments, each physical memory  24  may have more than one replica store, such that each replica store for the cache blocks of a particular physical memory  24  is physically distinct from and managed by a different local cache manager than the other replica stores. This may reduce exposure to failure of physical memory  24 , failure of a local cache manager  34 , or failure of a computing system. In particular embodiments in which each physical memory  24  has multiple replica stores, the location of each replica store may be chosen using a circular scheme; these embodiments may require that there is an ordered list of local cache managers  34 . As an example, each of a local memory cache manager&#39;s physical memory units may have their N replica stores hosted sequentially by physical memory units managed by the next N local cache managers. This disclosure contemplates any suitable manner of locating replica stores in clustered cache  22 . 
     The assignment of a replica store for a set of cache blocks (or other portion of physical memory  24 ) may occur or change upon a variety of conditions within clustered cache  22 . As an example, when membership in cache  22  changes, a new replica store may be created or an existing replica store may change ownership. If, for example, a computing system  26  or memory  24  joins clustered cache  22 , a new replica store may be created for the corresponding new cache blocks. Similarly, if a computing system  26  or memory  24  fails (or is automatically or manually reconfigured), an existing replica store (associated with the failing unit) may be absorbed as a fully functional part of clustered cache  22  and a new replica store may then be created. Additionally, if a new local cache manager  34  is associated with cache  22  or if an existing cache manager  34  fails or otherwise is disassociated with cache  22 , a new replica store may be created or an existing replica store may be changed. 
     Each replica store may include one or more replica blocks, with each replica block in a replica store corresponding to a cache block in a primary store (i.e., the portion of clustered cache  22  that the replica store is replicating). In particular embodiments, a replica block is created when the primary cache block becomes writeable. As an example, the primary cache block may contain data that was previously read in from auxiliary store  50  for client  32 . If client  32  subsequently issues a write command to the primary block, a replica block should be created. The client will not be able to proceed with this write to the primary block before the replica block is allocated. The replica block may be allocated by the local cache manager  34  that manages the primary block. In other embodiments, the replica block may be allocated by the local cache manager  34  that manages the replica store that will contain the replica block. Once the replica block is allocated, the client obtains a write reference and may proceed in writing to the primary block. As the client writes to the primary block, the replica block is populated with the data written by the client. The management of the writes to the replica block may be done by the local cache manager  34  that manages the primary block. The writes to a primary block and its replica block may, in certain embodiments, be dispatched by the local memory manger  34  proximately in time to reduce latency in completing a write back to a client  32 , for example. Additionally, in particular embodiments, a local memory manger  34  may keep records of pending write operations to primary blocks in its associated memory  24  and to the primary blocks&#39; replica blocks; these records may be stored in cache store  60  and may allow for recovery in case a connection to the replica store or stores for memory  24  are lost. 
     In particular embodiments, a replica block may be released when its corresponding primary block contains no “dirty” or unflushed data and when no client  32  holds a write reference to the primary block. The local cache manager  34  managing the primary block may then de-allocate or free the replica block of the replica store (either directly or in communication with the local cache manager  34  managing the replica store). In other embodiments, a replica block may be released when the primary block contains no dirty or unflushed data even if a client  32  still holds a write reference to the primary block. 
     In embodiments of clustered cache  22  including cache data replication functionality, client  32  is not required to issue a flush command on dirty cache blocks in order to prevent data loss, since each dirty cache block is replicated elsewhere in clustered cache  22 . However, it may still be desirable in particular embodiments for client  32  to retain write references to and maintain a list of its least recently used cache blocks to allow a local cache manager  34  to flush the least recently used dirty cache blocks to a backing store (e.g., auxiliary store  50 ), ask for release of the client&#39;s write references to those blocks, and free the replicas of those blocks. 
     Policy Example—Cache Solvency 
     In particular embodiments of clustered cache  22 , a solvency policy is applied. Maintaining cache solvency, generally, refers to maintaining a portion of the cache that has no client  32  references to it and that contains no dirty data. The cache blocks (or other units of memory  24 ) in cache  22  that satisfy these requirements may be referred to as the cache solvency pool. As an example implementation of a cache solvency policy, a cache solvency pool may be maintained by enforcing a budget for dirty data blocks and a budget of cache references that any client  32  may have at a given time for the portion of cache  22  managed by a particular local cache manager  34 . These budgets for dirty data and location references may be communicated to each client by the particular local cache manager. The budgets may change at any time; for example, if the size of the memory  24  changes or if another client  32  connects to local memory manger  34 . The local cache manager limits for dirty data and outstanding references may be divided among its clients. As an example, if local cache manager  34  has a hard dirty data budget of 50% (i.e., up to 50% of the data in its associated memory  24  may be dirty at a given time), and it has 5 clients  32  associated with it, then the cache manager may communicate a dirty data budget of 10% (of the total memory  24 ) to each of the five clients  32 . In this example, if any client exceeds dirty data limit of 10%, local cache manager  34  may communicate to that client that it should attempt to flush some of its existing dirty data. If, in this same example, any client hits the hard total dirty data budget of 50%, local cache manager may communicate to this client that it may no longer write to memory  24 . As another example, if local cache manager  34  has exceeded its accessible data or outstanding reference budget by 80 megabytes, and if it has 10 clients  32 , local cache manager  34  may communicate to each of the 10 clients that it would like each of them to release 8 megabytes worth of their data references to memory  24 . In this embodiment of the cache cluster  22  with cache solvency policy, it is up to each client  32  to tell local cache manager  34  when it may flush dirty data written by the client or when it may release references held by the client. As such, when the local cache manager  34  makes a request to a client, it is up to the client when the client will comply. In the example in which cache manager  34  requests each client to release 8 megabytes worth of data, it may be the case that certain clients comply immediately while others do not. Cache manager  34  may then reassess how much more data should be released in order to maintain its cache solvency. Once it has determined what that new number is (for example, 40 megabytes), cache manager  34  may again request each of its clients to release some fraction of this new amount (for example, 4 megabytes from each of 10 clients). This process of requesting the release of references and recalculating how much more is needed for solvency may repeat until cache manager  34  has achieved its solvency goals (as defined by its budgets). In particular implementations, local cache manager  34  may keep track (e.g. in cache store  60 ) of which clients it has made release requests of and how much has been released by each client. Clients may choose which references to release based on which references are for the least-recently-used cache blocks, as described above. It should be noted that, in certain implementations of this cache solvency policy, in order for local cache manager  34  to regain a cache block, all clients  32  with references to that cache block should release their references, and any dirty data for that block should first be flushed (before it may be released). 
     In a second example embodiment of clustered cache  22  utilizing a cache solvency policy, the local cache manager  34  is charged with flushing dirty data bits to auxiliary store  50  and with managing the amount of accessible data in memory  24  (e.g., the amount of data with outstanding references). In this implementation, there is an implicit hard limit on the amount of accessible data in that when memory  24  is full, no more references are available, and local cache manager  34  performs write-through or read-through functions. Like the first example embodiment of a cache solvency policy, local cache manager  34  may determine how much data needs to be “given up” (how many references need to be released) by clients  32  and may request each of these clients iteratively to release some fraction of the global amount. When clients  32  release data references to cache blocks with dirty bits on them, the local cache manager  34  may flush the dirty bits, as it is in charge of flushing in this implementation. As an example, local cache manager  34  may maintain a pipeline of in-flight I/O that may be flushed when it desires (e.g., in cache store  60 ). Local cache manager  34  may also maintain a flush queue for the least-recently-used cache blocks having dirty bits to determine which blocks to flush first. In particular embodiments, the flush queue managed by local cache manager  34  may keep track (for each cache block) when the cache block became dirty. If a cache block has been dirty for a certain amount of time, it may be moved to the front of the flush queue. In other embodiments, the flush queue may operate in a background fashion, in an opportunistic fashion (e.g., flush when there are no write references to a cache block having dirty data bits), or any other suitable manner. 
     Policy Example—Thin Write-Back Cache 
     If the first access by client  32  to an element in auxiliary store  50  is a write, then in a traditional write-back cache, a read from auxiliary store  50  would first occur, creating a cache block in clustered cache  22 . The cache block would then be written to by client  32 . In particular embodiments, clustered cache  22  may employ a thin write-back cache strategy that may avoid requiring that a read from auxiliary store  50  first occur before a client  32  may write to cache  22 . In one implementation, when a client  32  indicates that they would like to write to cache  22 , the client  32  is allowed (managed, e.g. by local cache managers  34 ) to directly write to an entry in cache  22 . That is, the cache block is allocated but data is not read in from auxiliary store  50 ; the client  32  writes to the allocated cache block. The local cache manager for the memory  24  in which cache block resides will maintain a mapping of all sectors (units of memory  24  that are smaller than a cache block) of all its cache blocks, e.g. in cache store  60 . The mapping of the sectors will contain information about which sectors are “dirty”—e.g., which sectors have been written to but have not been flushed to auxiliary store  50 . In one example sector map, the map is 64 bits, each bit corresponding to one of 64 sectors of a cache block; if the bit is a “1” then the corresponding sector may be “dirty,” and if the bit is a “0”, then the corresponding sector may be “clean.” If, at any point during its lifetime after being written, the cache block is read in from auxiliary store  50 , only a partial read will be done. That is, only the non-dirty sectors of the cache block will be read in from auxiliary store  50 . If, instead, before the cache block is ever read, it must be expired, only a partial write will be done. That is, only the dirty sectors of the cache block will be flushed from the cache block to the auxiliary store (as the other sectors of the cache block have not been written, nor do they contain any data read-in from auxiliary store). In addition to a dirty-sector mapping, the local cache manager  34  may also maintain a separate valid-sector mapping. The valid-sector mapping indicates which of the sectors of the cache block are valid or up-to-date (e.g., for reading by client  32 ). If, for example, after being written, a partial read is done to the cache block from auxiliary store  50 , those sectors read in from auxiliary store  50  will be considered valid and marked as such in the valid-sector mapping (e.g., using a 64-bit mapping similar to the dirty-sector mapping). Generally speaking, a sector may be considered valid if it is up-to-date. That is, if a sector is dirty, then the sector may also be valid (because it is up-to-date and valid for reading by a client even though the data has not yet been flushed to the auxiliary store  50 ). Post-flush, there may be no dirty sectors in a cache block, but the previously-dirty sectors (which are as-yet untouched by client  32 ) are still valid sectors. The management of the sector maps may be done by local cache manager  34 , either with or without knowledge (or assistance provided) by client  32 . In particular implementations, once an entire cache block is considered “valid” in the valid-sector map, then a flag may be set, and client  32  may directly access this block in cache  22  for a read without having to interact first with local cache manager  34 . 
     CONCLUSION 
     Herein, a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other integrated circuits (ICs) (such, as for example, field-programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs), magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM-drives, SECURE DIGITAL cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate. 
     Herein, “or” is inclusive and not exclusive, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A or B” means “A, B, or both,” unless expressly indicated otherwise or indicated otherwise by context. Moreover, “and” is both joint and several, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A and B” means “A and B, jointly or severally,” unless expressly indicated otherwise or indicated otherwise by context. 
     This disclosure encompasses all changes, substitutions, variations, alterations, and modifications to the example embodiments herein that a person having ordinary skill in the art would comprehend. Moreover, although this disclosure describes and illustrates respective embodiments herein as including particular components, elements, functions, operations, or steps, any of these embodiments may include any combination or permutation of any of the components, elements, functions, operations, or steps described or illustrated anywhere herein that a person having ordinary skill in the art would comprehend. Furthermore, reference in the appended claims to an apparatus or system or a component of an apparatus or system being adapted to, arranged to, capable of, configured to, enabled to, operable to, or operative to perform a particular function encompasses that apparatus, system, component, whether or not it or that particular function is activated, turned on, or unlocked, as long as that apparatus, system, or component is so adapted, arranged, capable, configured, enabled, operable, or operative.