US20150244795A1 - Data syncing in a distributed system - Google Patents

Data syncing in a distributed system Download PDF

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US20150244795A1
US20150244795A1 US14/186,847 US201414186847A US2015244795A1 US 20150244795 A1 US20150244795 A1 US 20150244795A1 US 201414186847 A US201414186847 A US 201414186847A US 2015244795 A1 US2015244795 A1 US 2015244795A1
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data
volume
block
source
blocks
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US14/186,847
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Jared CANTWELL
Bill MINCKLER
Joe ROBACK
Jim WITTIG
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NetApp Inc
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SolidFire Inc
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Priority to US14/186,847 priority Critical patent/US20150244795A1/en
Assigned to SOLIDFIRE, INC. reassignment SOLIDFIRE, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: CANTWELL, Jared, WITTIG, James Philip, MINCKLER, WILLIAM, ROBACK, Joe
Priority to PCT/US2015/016625 priority patent/WO2015127083A2/en
Priority to US14/684,929 priority patent/US10628443B2/en
Publication of US20150244795A1 publication Critical patent/US20150244795A1/en
Assigned to NETAPP, INC. reassignment NETAPP, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: SOLIDFIRE, INC.
Priority to US16/853,660 priority patent/US11386120B2/en
Abandoned legal-status Critical Current

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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G06F17/30575
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00Data switching networks
    • H04L12/02Details
    • H04L12/14Charging, metering or billing arrangements for data wireline or wireless communications
    • H04L12/1403Architecture for metering, charging or billing
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14Network analysis or design
    • H04L41/145Network analysis or design involving simulating, designing, planning or modelling of a network
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/01Protocols
    • H04L67/10Protocols in which an application is distributed across nodes in the network
    • H04L67/1095Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/50Network services
    • H04L67/535Tracking the activity of the user
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/10Office automation; Time management
    • G06Q10/109Time management, e.g. calendars, reminders, meetings or time accounting
    • G06Q10/1093Calendar-based scheduling for persons or groups
    • G06Q10/1095Meeting or appointment
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce
    • G06Q30/01Customer relationship services
    • G06Q30/015Providing customer assistance, e.g. assisting a customer within a business location or via helpdesk
    • G06Q30/016After-sales

Definitions

  • a client's data may be stored in a volume.
  • a unit of data for example a file (or object), is comprised of one or more storage units (e.g. bytes) and can be stored and retrieved from a storage medium such as disk or RAM in a variety of fashions.
  • disk drives in storage systems are divided into logical blocks that are addressed using logical block addresses (LBAs).
  • LBAs logical block addresses
  • an entire file can be stored in a contiguous range of addresses on the storage medium and be accessed given the offset and length of the file.
  • file systems store files by dividing them into blocks or extents of a fixed size, storing each block in a contiguous section of the storage medium, and then maintaining a list or tree of the blocks that correspond to each file.
  • Some storage systems such as write-anywhere file layout (WAFL), logical volume manager (LVM), or new technology file system (NTFS), allow multiple objects to refer to the same blocks, typically through a tree structure, to allow for efficient storage of previous versions or “snapshots” of the file system.
  • data for a single file or object may be distributed between multiple storage devices, either by a mechanism like RAID which combines several smaller storage media into one larger virtual device, or through a distributed storage system such as Lustre, General Parallel File System, or GlusterFS.
  • FIG. 1 depicts a simplified system for a storage system in accordance with an illustrative implementation.
  • FIG. 2A depicts a hash tree in accordance with an illustrative implementation.
  • FIG. 2B depicts the hash tree illustrated in FIG. 2A , with updated node hashes, in accordance with an illustrative implementation.
  • FIG. 2C depicts the hash tree illustrated in FIG. 2A , with newly added leaves, in accordance with an illustrative implementation.
  • FIG. 3 shows a flow diagram of an incremental block level backup procedure in accordance with an illustrative implementation.
  • FIG. 4 depicts a distributed storage system in accordance with an illustrative implementation.
  • FIG. 5 shows a flow diagram for replicating data in accordance with an illustrative implementation.
  • one innovative aspect of the subject matter described below can be embodied in methods for receiving a start replication message from a source system to replicate data of a source volume to a replicated volume on a replica server.
  • the replicated volume comprises a copy of data of the source volume.
  • the source system forwards input/output (I/O) requests to the replica server after the start replication message is sent.
  • a data structure associated with units of data of the replicated volume is initialized.
  • a write request is received from the source system that includes write data associated a unit of data of the replicated volume.
  • the source system wrote the write data to the source volume based upon the write request.
  • the write data is written to the replicated volume.
  • the data structure is updated to indicate the write data has been written after the receipt of the start replication message.
  • Source metadata associated with the source volume is received.
  • the metadata includes an ordered list of block identifiers for data blocks of the source volume. Each block identifier is used to access a data block.
  • the source metadata is compared with prior metadata associated with a prior point-in-time image of the source volume to determine blocks of data that have changed since the prior point-in-time image of the source volume.
  • a first block of the blocks of data is determined to not be retrieved based upon the data structure.
  • a second block of the blocks of data is determined to be retrieved based upon the data structure.
  • the second block is received from the source system and written to the replicated volume.
  • Other embodiments of this aspect include corresponding systems, apparatuses, and computer-readable media, configured to perform the actions of the method.
  • FIG. 1 depicts a simplified system for incremental block level backup of a storage system 100 in accordance with an illustrative implementation.
  • System 100 includes a client layer 102 , a metadata layer 104 , a block server layer 106 , storage 116 , and storage 120 .
  • client layer 102 includes one or more clients 108 a - 108 n .
  • Clients 108 include client processes that may exist on one or more physical machines.
  • client the action being performed may be performed by a client process.
  • a client process is responsible for storing, retrieving, and deleting data in system 100 .
  • a client process may address pieces of data depending on the nature of the storage system and the format of the data stored. For example, the client process may reference data using a client address.
  • the client address may take different forms. For example, in a storage system that uses file storage, client 108 may reference a particular volume or partition, and a file name. With object storage, the client address may be a unique object name.
  • the client address may be a volume or partition, and a block address.
  • Clients 108 communicate with metadata layer 104 using different protocols, such as small computer system interface (SCSI), Internet small computer system interface (ISCSI), fibre channel (FC), common Internet file system (CIFS), network file system (NFS), hypertext transfer protocol (HTTP), hypertext transfer protocol secure (HTTPS), web-based distributed authoring and versioning (WebDAV), or a custom protocol.
  • SCSI small computer system interface
  • ISCSI Internet small computer system interface
  • FC common Internet file system
  • CIFS common Internet file system
  • NFS network file system
  • HTTP hypertext transfer protocol
  • HTTPS hypertext transfer protocol secure
  • WebDAV web-based distributed authoring and versioning
  • Metadata layer 104 includes one or more metadata servers 110 a - 110 n . Performance managers 114 may be located on metadata servers 110 a - 110 n .
  • Block server layer 106 includes one or more block servers 112 a - 112 n . Block servers 112 a - 112 n are coupled to storage 116 , which stores volume data for clients 108 . Each client 108 may be associated with a volume. In one implementation, only one client 108 accesses data in a volume; however, multiple clients 108 may access data in a single volume.
  • Storage 116 can include multiple solid state drives (SSDs).
  • storage 116 can be a cluster of individual drives coupled together via a network. When the term “cluster” is used, it will be recognized that cluster may represent a storage system that includes multiple disks that may not be networked together.
  • storage 116 uses solid state memory to store persistent data. SSDs use microchips that store data in non-volatile memory chips and contain no moving parts. One consequence of this is that SSDs allow random access to data in different drives in an optimized manner as compared to drives with spinning disks. Read or write requests to non-sequential portions of SSDs can be performed in a comparable amount of time as compared to sequential read or write requests.
  • non-sequentially storing data in storage 116 is based upon breaking data up into one more storage units, e.g., data blocks.
  • a data block therefore, is the raw data for a volume and may be the smallest addressable unit of data.
  • the metadata layer 104 or the client layer 102 can break data into data blocks.
  • the data blocks can then be stored on multiple block servers 112 .
  • Data blocks can be of a fixed size, can be initially a fixed size but compressed, or can be of a variable size.
  • Data blocks can also be segmented based on the contextual content of the block. For example, data of a particular type may have a larger data block size compared to other types of data.
  • Maintaining segmentation of the blocks on a write (and corresponding re-assembly on a read) may occur in client layer 102 and/or metadata layer 104 . Also, compression may occur in client layer 102 , metadata layer 104 , and/or block server layer 106 .
  • data blocks can be stored to achieve substantially even distribution across the storage system.
  • even distribution can be based upon a unique block identifier.
  • a block identifier can be an identifier that is determined based on the content of the data block, such as by a hash of the content.
  • the block identifier is unique to that block of data. For example, blocks with the same content have the same block identifier, but blocks with different content have different block identifiers.
  • the values of possible unique identifiers can have a uniform distribution. Accordingly, storing data blocks based upon the unique identifier, or a portion of the unique identifier, results in the data being stored substantially evenly across drives in the cluster.
  • client data e.g., a volume associated with the client
  • every drive in the cluster is involved in the read and write paths of each volume. This configuration balances the data and load across all of the drives. This arrangement also removes hot spots within the cluster, which can occur when client's data is stored sequentially on any volume.
  • having data spread evenly across drives in the cluster allows a consistent total aggregate performance of a cluster to be defined and achieved. This aggregation can be achieved, since data for each client is spread evenly through the drives. Accordingly, a client's I/O will involve all the drives in the cluster. Since, all clients have their data spread substantially evenly through all the drives in the storage system, a performance of the system can be described in aggregate as a single number, e.g., the sum of performance of all the drives in the storage system.
  • Block servers 112 and slice servers maintain a mapping between a block identifier and the location of the data block in a storage medium of block server 112 .
  • a volume includes these unique and uniformly random identifiers, and so a volume's data is also evenly distributed throughout the cluster.
  • Metadata layer 104 stores metadata that maps between client layer 102 and block server layer 106 .
  • metadata servers 110 map between the client addressing used by clients 108 (e.g., file names, object names, block numbers, etc.) and block layer addressing (e.g., block identifiers) used in block server layer 106 .
  • Clients 108 may perform access based on client addresses.
  • block servers 112 store data based upon identifiers and do not store data based on client addresses. Accordingly, a client can access data using a client address which is eventually translated into the corresponding unique identifiers that reference the client's data in storage 116 .
  • Entities in system 100 may be virtualized entities.
  • multiple virtual block servers 112 may be included on a machine.
  • Entities may also be included in a cluster, where computing resources of the cluster are virtualized such that the computing resources appear as a single entity.
  • One or more backup servers 118 a - 118 n can interface with the metadata layer 104 .
  • Backup servers 118 can interface directly with block servers 112 .
  • Backup servers 118 a - 118 n are coupled to storage 120 , which stores backups of volume data for clients 108 .
  • Storage 120 can include multiple hard disk drives (HDDs), solid state drives (SSDs), hybrid drives, or other storage drives.
  • HDDs hard disk drives
  • SSDs solid state drives
  • storage 120 can be a cluster of individual drives coupled together via a network.
  • Backup servers 118 can store backup copies of the data blocks of storage 116 according to any number of formats in storage 120 , and translation from the format of the data blocks of storage 116 may occur.
  • Data may be transferred to and from backup servers 118 using different protocols, such as small computer system interface (SCSI), Internet small computer system interface (ISCSI), fibre channel (FC), common Internet file system (CIFS), network file system (NFS), hypertext transfer protocol (HTTP), hypertext transfer protocol secure (HTTPS), web-based distributed authoring and versioning (WebDAV), or a custom protocol.
  • SCSI small computer system interface
  • ISCSI Internet small computer system interface
  • FC common Internet file system
  • CIFS common Internet file system
  • NFS network file system
  • HTTP hypertext transfer protocol
  • HTTPS hypertext transfer protocol secure
  • WebDAV web-based distributed authoring and versioning
  • Compression and data de-duplication may occur in backup servers 118 a - 118 n.
  • the servers of metadata layer 104 store and maintain metadata that maps between client layer 102 and block server layer 106 , where the metadata maps between the client addressing used by clients 108 (e.g., file names, volume, object names, block numbers, etc.) and block layer addressing (e.g., block identifiers) used in block server layer 106 .
  • the metadata includes a list of block identifiers that identifies blocks in a volume. The list may be structured as an ordered list corresponding to a list of blocks. The list may also be structured as the leaves of a hash tree.
  • the block identifiers of the metadata are the same block identifiers as used throughout system 100 as described above.
  • the block identifiers may be hexadecimal numbers, but other representations may be used. Additional metadata may also be included, such as inode numbers, directory pointers, modification dates, file size, client addresses, list details, etc.
  • the block identifiers uniquely identify the data of a block and are a hash based on the content of the data block.
  • Backup servers 118 are generally configured to create backups of block level data of a volume that is stored in storage 116 of block server layer 106 . Backup servers 118 may create backups of all of the volume data of block server layer 106 or backup servers 118 may create backups of one or more particular volumes (e.g., a volume of a client 108 ). Backups may be full backups of all data, or they may be incremental backups (e.g., data that has changed since a previous backup).
  • a backup server 118 retrieves a copy of metadata from metadata server 110 for a client volume.
  • the metadata includes a list of block identifiers associated with data blocks of the volume.
  • the metadata includes an ordered list structure of block identifiers.
  • the ordered list is structured as the leaves of a hash tree (e.g., a Merkle tree, etc.) and the metadata includes the hash tree.
  • the metadata is used by backup server 118 to retrieve a copy of all of the data blocks of the client volume in order to create an initial backup of the data blocks.
  • the data blocks are retrieved from storage 116 by sending a request for the data to a metadata server 110 .
  • the requested data is based on the data block identifiers.
  • a request may include a list of the block identifiers of blocks desired to be backed up.
  • backup server 118 may calculate the LBAs of blocks desired to be backed up. For example, because each block identifier can represent a known amount of data (e.g., a 4 k block, etc.), an LBA of a block can be calculated based on the location of the block identifier in the ordered list of block identifiers associated with the volume. For example, the position of a block identifier in the ordered list can be used along with the block size to determine the LBA of the data block. As described below, the tree structure can also be used to determine the data blocks that have changed after a previous backup.
  • a request from backup server 118 may include a list of LBAs of blocks to be backed up.
  • the metadata server 110 routes the request to a block server 112 , which provides the requested data to metadata server 110 .
  • Metadata server 110 then routes the requested data to the backup server 118 .
  • This arrangement allows the servers of metadata layer 104 to facilitate data transmission between block server layer 106 and the backup servers 118 .
  • backup servers 118 may be configured to communicate directly with servers of block server layer 106 . Upon retrieval of the requested data, the backup server 118 stores the data in storage 120 .
  • the data may be stored in storage 120 according to any of the methods discussed herein.
  • Backup server 118 may create and maintain statistics and snapshot data corresponding to a particular backup operation. The snapshot data may be used later during a data restoration operation, or during a future backup operation.
  • Backup server 118 can also store a copy of the metadata used during a particular backup operation.
  • the metadata is not stored on the backup server 118 . Rather, the metadata is stored on another storage device, for example, one or more metadata servers, one or more block servers, or one or more devices remote from the backup system. As a result of the initial backup operation, a complete backup of the data of a client volume is created and stored in storage 120 .
  • a backup server 118 retrieves the current metadata from metadata server 110 for a client volume. The backup server 118 can then compare the current metadata from metadata server 110 with a version of stored metadata on backup server 118 (e.g., the version of metadata stored during the most recent backup operation, or the initial version of the metadata stored during the initial backup, etc.). In an implementation where the metadata includes an ordered list of block identifiers, the backup server 118 can compare the block identifiers of the two versions of metadata node-by-node. For example, the current list node corresponding to a first block of data is compared to the stored list node corresponding to the first block of data, and each node of the ordered list is traversed and compared.
  • the block identifiers are hashes based on content of a corresponding data block, a difference in hash values for corresponding nodes indicates that the data of the block has been changed/updated since the prior backup.
  • the block identifiers are integral to storage system 100 and maintained as described herein, the block identifiers can be compared in their native format and immediately used without the need to compute the hash values.
  • the metadata includes a hash tree and the ordered list of block identifiers are structured as the leaves of the hash tree, additional performance gains may be realized.
  • Such a hash tree is generally a tree data structure in which every non-leaf node includes the hash of its children nodes.
  • backup server 118 can retrieve the updated blocks of data from storage 116 by sending a request for the changed data block to the metadata server 110 .
  • the metadata server 110 can facilitate the transfer of data from the block server layer 106 .
  • the backup server 118 stores the data in storage 120 .
  • the backup server 118 also stores the current metadata from metadata server 110 used in the incremental backup operation.
  • any number of incremental backup operations may be performed, during which the current metadata from metadata server 110 may be compared to previously stored metadata on backup server 118 (e.g., the stored metadata from a prior backup operation).
  • Backup servers 118 may also provide an application programming interface (API) in order to allow clients 108 or traditional data backup software to interface with the backup systems described herein.
  • API application programming interface
  • the API may allow backup servers 118 to send statistics related to backed up data and backup operations to and from clients 108 or traditional backup software.
  • the API may allow backup servers 118 to receive a request to initiate a backup operation.
  • the API can also allow for backup operations to be scheduled as desired by clients 108 or as controlled by data backup software.
  • Other API functionality is also envisioned.
  • a hash tree 200 a is shown in accordance with an illustrative implementation.
  • the hash tree 200 a may be a hash tree that is provided by a metadata server 110 to a backup server 118 in an initial or incremental backup operation as discussed above.
  • hash tree 200 a (and hash trees described herein) may have any number of child nodes/branches.
  • Hash tree 200 a represents the data of a particular volume, and can be provided along with additional metadata describing details related to the tree structure.
  • the metadata may include statistics regarding node counts, leaf-node counts, tree-depth, indexes to sub-trees, etc.
  • Hash tree 200 a includes leaves 202 a - d , internal nodes 204 a - b , and root node 206 .
  • Leaves 202 a - d store block identifies B 1 -B 4 , respectively.
  • leaves 202 a - d may be structured as an ordered list that is indexed by its parent nodes, which in this example are internal nodes 204 .
  • Block identifiers B 1 -B 4 are identifiers as described herein (e.g., a hash of the corresponding data block's content), and each uniquely identify a particular data block of the volume.
  • Hash tree 200 a further includes non-leaf internal nodes 204 a - b and non-leaf root node 206 .
  • the value stored by each non-leaf node is the hash of that node's children values.
  • hash H 1 is the hash of block identifiers B 1 and B 2
  • hash H 2 is the hash of block identifiers B 3 and B 4
  • hash H 3 is the hash of hashes H 1 and H 2 .
  • backup server 118 can walk the tree, or traverse the ordered list of leaves 202 a - d to determine that the data blocks corresponding to block identifiers B 1 -B 4 should be retrieved to be backed up.
  • a copy of hash tree 200 a (and any accompanying metadata) is stored by backup server 118 when a backup operation is performed.
  • hash tree 200 a of FIG. 2 a is shown at a later time instance, as hash tree 200 b .
  • hash tree 200 a may have been provided by metadata server 110 during an initial backup operation and stored by the backup server 118
  • hash tree 200 b may have been provided by metadata server 110 during a subsequent incremental backup operation.
  • Both hash trees 200 a - b represent the data stored on a particular volume.
  • the block identifier B 3 of leaf node 202 c has changed to become block identifier B 3 ′ at some time since the previous backup. For example, new or updated data may have been written to the block referenced by block identifier B 3 .
  • backup server 118 may walk the hash tree 200 b , and compare the nodes of hash tree 200 b to corresponding nodes of hash tree 200 a . A difference between corresponding non-leaf node hashes indicates that a block identifier (and therefore block data) below that non-leaf node has changed.
  • backup server 118 may compare hash tree 200 b to hash tree 200 a as follows (although analysis performed by backup server 118 is not limited to the following operations or order of operations):
  • backup server 118 may proceed to retrieve the data based on the block identifier(s) that indicate data has changed, and has not yet been backed up.
  • backup server 118 may send a request to a metadata server 110 for the data block identified by block identifier B 3 ′.
  • backup server 118 Upon receipt of the data block, backup server 118 stores the data block as a backup, and stores hash tree 200 b (along with any accompanying metadata) for use in future backup and/or restoration operations.
  • backup server 118 may retrieve the metadata from a metadata server 110 by requesting only child nodes whose parent node has changed. For example, starting with the root, if the root node has changed the children of the root node can then be requested. These nodes can then be compared to corresponding nodes in the previously stored tree to determine if those have changed. Children of any node that has changed can then be retrieved. This process can be repeated until leaf nodes are retrieved.
  • hash tree 200 b may be the current metadata from metadata server 110
  • hash tree 200 a may be stored metadata from a previous backup operation.
  • Backup server 118 may first retrieve root node 206 and analyze it to determine that hash H 3 ′ is different from its previous value of H 3 . In response, backup server 118 may then request nodes 204 a - b from interior node level 204 . Node 204 a is analyzed to determine that hash H 1 has not changed, and leaf nodes 202 a - b may be skipped from further requests/analysis. Node 204 b is analyzed to determine that hash H 2 ′ is different from its previous value of H 2 , and thus backup server 118 may proceed to request appropriate nodes of leaf level 202 (leaves 202 c - d ).
  • Block identifier B 3 ′ is different from its previous value of B 3 and that the data block corresponding to block identifier B 3 ′ needs to be backed up.
  • This implementation may allow for performance increases by minimizing data that is transmitted between backup server 118 and metadata server 110 during the retrieval of metadata.
  • backup servers 118 implementations configured to utilize metadata of an ordered list of block identifiers, any newly added block identifiers (corresponding to the new data blocks) may be appended to the end of the ordered list.
  • backup server 118 can determine the newly added data blocks that must be backed up based on the additional list elements. The backup operation may proceed as described above with respect to the remaining elements.
  • FIG. 2C depicts the result of an increased volume size for implementations configured to utilize metadata of a hash tree.
  • Hash tree 200 c is based on hash tree 200 a (which is included as a subtree and is denoted by a dashed box).
  • Leaves 202 e - f have been newly added to the hash tree and include block identifiers B 5 -B 6 , which correspond to the newly added data blocks of the increased volume size.
  • hash tree 200 a is restructured such that root node 206 becomes internal node 206 a , and a new root node 208 is created. Further, internal nodes 206 b and 204 c are added to maintain the tree structure.
  • Hashes H 4 -H 6 are calculated based on the respective child values as described above.
  • backup server 118 can determine the newly added data blocks that must be backed up based on a new root node or additional leaves.
  • an implementation may make use of additional metadata that includes the indexes of the root nodes of previously stored trees. In this manner, backup server 118 may access the indexes to locate and compare the root node of a prior tree with the corresponding internal node of the current tree (e.g., root node 206 can be compared to internal node 206 a .). If the comparison indicates that the hashes have not changed, then backup server 118 may skip analyzing the subtree of the internal node, and a performance gain may be realized.
  • backup server 118 may be desirable by clients 108 or an administrator of system 100 to reduce the volume size assigned to a client 108 by removing data blocks of storage space.
  • backup server 118 implementations configured to utilize metadata of an ordered list of block identifiers, any removed block identifiers (corresponding to removed data blocks) may be removed from the end of the ordered list.
  • backup server 118 can determine the backed up data blocks that may be removed based on the additional list elements in the stored list from the prior backup. The backup operation may proceed as described above with respect to the remaining elements.
  • the backup server 118 may compare the trees (e.g. depth of the trees, leaf node count, etc.) to determine that there has been a change in volume size.
  • the size of the volume can be part of the metadata received by the backup servers, and this metadata can be compared to a previously received volume size to determine that a change in volume has occurred.
  • the backup server may then determine the position of the current tree within the stored hash tree. After locating the position of the current root node, the leaf nodes (and corresponding parent nodes) that are not within the subtree of the current root node can be ignored. Once the corresponding root nodes have been determined, the backup operation may then proceed as described above with respect to the remaining nodes.
  • FIG. 3 shows a simplified flow diagram of an incremental block level backup procedure 300 , in accordance with an embodiment. Additional, fewer, or different operations of the procedure 300 may be performed, depending on the particular embodiment.
  • the procedure 300 can be implemented on a computing device.
  • the procedure 300 is encoded on a computer-readable medium that contains instructions that, when executed by a computing device, cause the computing device to perform operations of the procedure 300 .
  • at least a portion of the various types of functions, operations, actions, and/or other features provided by the incremental block level backup procedure may be implemented at one or more nodes and/or volumes of the storage system.
  • metadata for a particular volume is retrieved (e.g., from a metadata server).
  • a backup sever may initiate a backup operation and retrieve initial metadata as described above.
  • the backup server may be responding to a request to initiate a backup operation.
  • a client or backup software may submit a request via an API to perform a backup at a certain time.
  • the backup server may be performing a backup according to a schedule (e.g., nightly backups, weekly backups, client-specified backups, etc.).
  • the initial backup of the data blocks of the volume is created.
  • the metadata provides the block identifiers corresponding to the volume.
  • the metadata may include an ordered list of block identifiers, a hash tree based on block identifiers, and other related data.
  • the block identifiers are used to retrieve the corresponding data blocks to be backed up.
  • the backup server may analyze the metadata in order to request the transmission of and retrieve particular data blocks to be backed up.
  • the request may be sent to the metadata server, which can facilitate the transmission of data from a block server.
  • the backup server may retrieve the data blocks directly from the block server.
  • the initial backup is a backup of all of the data of the volume as specified by the metadata.
  • the metadata used for the initial backup is stored for future use.
  • an incremental backup of the volume is initiated by retrieving the current metadata.
  • the backup server may retrieve updated metadata, which has been maintained by the metadata server to be current with the data blocks of the volume.
  • metadata may be retrieved from a remote storage device.
  • the current metadata is compared to other metadata (e.g., the metadata from the immediately preceding backup operation, the metadata from the initial backup operation, the metadata from a remote device, etc.).
  • the backup server may analyze the metadata to determine changes in block identifiers as discussed above. Based on any changed block identifiers found during the analysis, in an operation 312 , an incremental backup is created. For example, based on the identifiers of the changed data blocks, the backup server may retrieve only the changed data blocks to be backed up.
  • the backup server may store received data blocks as described herein.
  • the metadata used for the incremental backup is stored for future use.
  • the backup server may also generate additional metadata related to the backup procedure, including statistics to the amount of data backed up, the elapsed time of the backup process, etc. This process may repeat any number of times to create any number of incremental backups, as indicated by operation 316 .
  • the retrieval of the metadata and the comparison of the metadata to other metadata is performed by a device other than the backup server (e.g., by one or more devices of the storage system).
  • a storage device remote from the backup server may access metadata on the storage device, or may retrieve the metadata from another device, for example, from the metadata server.
  • the storage device may analyze the metadata to determine changes in block identifiers as discussed above. Based on any changed block identifiers found during the analysis, an incremental backup can be created by transferring data to the backup server. For example, based on the identifiers of the changed data blocks, the storage device may transfer only the changed data blocks to the backup server to be backed up.
  • the backup server may store received data blocks as described herein.
  • the metadata used for the incremental backup can be stored by the storage device or can be transferred to another device (e.g., the metadata server) to be stored for future use.
  • data can synced/replicated to another location.
  • data from a source system can be copied to a replica server.
  • Data can be replicated locally, to another volume in its cluster, to another cluster, to a remote storage device, etc.
  • Data that can be replicated includes, but is not limited to, block server data, metadata server data, etc.
  • Replicated data is a representation of the data on the source system at a particular point in time.
  • the replication process does not stop incoming I/O operations.
  • writes that occur during the replication must be properly handled to avoid mismatches in data between the live data and the corresponding replicated data.
  • FIG. 4 depicts a distributed storage system 400 in accordance with an illustrative implementation.
  • the storage system 400 stores live client data and may be configured as discussed above regarding system 100 (e.g., including client layer 102 , metadata layer 104 , block server layer 106 , and storage).
  • the storage system 400 can also include one or more replica servers 418 a - 418 n .
  • Replica servers 418 a - 418 n can interface with the metadata and/or block servers of the storage system 400 in order to maintain synchronized (replicated) copies of data stored by the storage system 400 .
  • Replica servers 418 a - 418 n are coupled to storage 420 , which may store backups of volume data (e.g., backups of block level data of a client volume), synchronized data of client volume, snapshots of a client volume, and associated metadata.
  • Storage 420 may include multiple hard disk drives (HDDs), solid state drives (SSDs), hybrid drives, or other storage drives.
  • HDDs hard disk drives
  • SSDs solid state drives
  • storage 420 can be a cluster of individual drives coupled together via a network.
  • Replica servers 418 can store backup copies of the data blocks of storage system 400 according to any number of formats in storage 420 , and translation from the format of the data blocks may occur.
  • a replica server 418 maintains a live synchronized copy of data blocks of a client volume (e.g., a mirror copy of the client volume). To maintain synchronization, requests to write data that are provided by a client to storage system 400 may also be transmitted to the replica server 418 . In this manner, data written to storage system 400 can be synchronized and stored on replica server 418 in real-time or semi real-time. Synchronization of volume data on replica server 418 includes synchronizing the metadata of storage system 400 that identifies blocks in a client volume. As discussed above, metadata servers of the storage system store metadata that includes a list of block identifiers that identifies blocks in a volume.
  • the block identifiers may be hexadecimal numbers, and other representations may be used. Additional metadata may also be included (e.g., inode numbers, directory pointers, modification dates, file size, client addresses, list details, etc.).
  • the block identifiers uniquely identify the data of a block and are a hash based on the content of the data block.
  • the metadata includes an ordered list structure of block identifiers.
  • the ordered list is structured as the leaves of a hash tree (e.g., a Merkle tree, etc.) and the metadata includes the hash tree.
  • replica server 418 can maintain a live synchronization tree that is updated to parallel the a tree maintained by a metadata server of storage system 400 for a particular client volume.
  • FIG. 5 shows a flow diagram for replicating data in accordance with an illustrative implementation.
  • Replication begins with a replica server receiving a start replication message from a source system ( 502 ).
  • the replica server Upon receipt of the start replication message, the replica server initiates a data structure that will be used to track writes that occur during the replication process ( 504 ).
  • the data structure is a bit field where each bit represents a single unit of information, e.g., a block, a sub-block, etc. Each bit in the bit field represents if a particular unit has been written to after the start of the replication processes. In this embodiment, the bit field will be initialized to 0.
  • the source system sends over replication data to the replica server. Similar to the block level backup embodiments, merkle trees can be used to minimize the amount of data that is required to be transferred between the source system and the replica server.
  • the source system will handle the writes and while the replication process is active will also send the writes to the replica server.
  • the replica server can receive an I/O request to write a block of data ( 550 ). Upon receipt, the replica server can write the block of data ( 552 ) and will also update the bit associated with the block in the bit field to 1 ( 554 ). After the bit is set, the data write on the replica server is complete.
  • the replica server determines which blocks of data are needed from the source system ( 506 ). For example, a merkle tree comparison as described above can be used to determine blocks of data that have changed since a previous point-in-time image. One or more of the changed blocks of data, however, may have been changed again since the start of the replication process. Accordingly, the data will have already been sent to the replica server and requesting this data again is unneeded.
  • the bit field can be checked to determine if the block has already been received ( 508 ). If the block has not been updated, then the block of data is requested from the source system ( 510 ). The block is received ( 512 ) and written to storage.
  • the replication system can send a message to the source system indicating that replication is complete. Upon receipt, the source system can stop forwarding I/O to the replication system.
  • a block is the smallest amount of data that is written to storage in a single write operation.
  • a block can be divided into smaller sub-blocks, such that each unit of a block can be written to separately.
  • a block can be 4 kilobytes in size and broken down into sixteen 256 byte sub-blocks.
  • the data structure corresponds to the sub-blocks and not the blocks.
  • a write to a sub-block can be received.
  • the write command can include the data for the entire block or just the sub-block of data.
  • the write can update a cache that is associated with the sub-block or could write the sub-block to storage.
  • the block that contains the sub-block is retrieved and the sub-block is updated appropriately.
  • the Merkle tree comparison can be used to determine that the block with the updated sub-block needs to be retrieved from the source system. For example, another sub-block may have been update from the previous replication. The entire block can be retrieved. The corresponding block on the replica server is retrieved and updated. To update the corresponding block on the replica server, the data structure is used to update each sub-block from the block retrieved from the source system. For sub-blocks where the data structure indicates that the sub-block has been updated during the replication process, the sub-block is not updated since it already has the latest data.
  • the replica server can determine if all the sub-blocks of a block have been updated during the replica process. In this case, the replica server has already replicated this block and there is no need to request that block of data from the source system.
  • replica servers 418 a - 418 n can be configured to create point-in-time images of components of the data of storage system 400 .
  • each point-in-time image includes corresponding metadata (e.g., a hash tree) that identifies the blocks of the point-in-time image.
  • the hash tree of a point-in-time image is based on the block identifiers of the data stored for the point-in-time image.
  • a replica server 418 may create one or more point-in-time images of a component of the data of storage system 400 , and each point-in-time image may be created according a defined schedule, or on demand (e.g., in response to a client demand, or as demanded by an administrator of storage system 400 , etc.).
  • the source system may also create various copies/replicas of a volume locally. For example, every day a replica of a volume can be scheduled.
  • a remote replication system may only replicate a subset of the replicas that are local to the source system. For example, a remote replication system can request a single local copy every week rather than each of the daily local replicas. In another embodiment, the remote replication system can make a replica of the current live volume and ignore any other local replicas of the volume.
  • replica server 418 may be brought back online and resume synchronizing volume data with storage system 400 .
  • the data of replica server 418 may be out of sync with the volume data of storage system 400 .
  • replica server 418 may retrieve the data that is needed from storage system 400 to re-synchronize with the live volume data of storage system 400 .
  • replica server 418 may implement one or more techniques of the block level incremental backup process to synchronize the volume data.
  • replica server 418 can retrieve the metadata for a live volume (e.g., a tree corresponding to the live volume as maintained by a metadata server). Replica server 418 may then analyze versions of metadata (e.g., comparing the out-of-date synchronization tree of replica server 418 and the retrieved live volume tree). Based on this analysis, replica server 418 can determine changed data blocks of the volume and what blocks needs to be retrieved from storage system 400 to synchronize the volume data. The replica server 418 may request any changed data blocks from storage system 400 and the retrieved blocks may be stored. As replica server 418 is synchronizing its volume data, write requests may still be received and the point-in-time image can still be created.
  • a live volume e.g., a tree corresponding to the live volume as maintained by a metadata server.
  • Replica server 418 may then analyze versions of metadata (e.g., comparing the out-of-date synchronization tree of replica server 418 and the retrieved live volume tree). Based on this analysis, replica server 4
  • a data block may not yet be available in the data of replica server 418 to be stored in the new point-in-time image.
  • the comparison of the metadata of the new tree with the metadata of the live tree may indicate that a block identifier (and therefore block data) has changed.
  • the changed block may not yet be synchronized in the volume data of replica server 418 .
  • replica server 418 may retrieve the changed block data directly from the storage system 400 (as opposed to pointing to or retrieving the changed block data from the synchronized volume data of replica server 418 as discussed above).
  • the replication can be verified. In one embodiment, this is done by the source system sending to the replica system one or more merkle tree nodes. The replica system can then compare the received merkle tree nodes with the corresponding merkle tree nodes of the replicated copy of the source volume. If any corresponding nodes do not match, the data was not properly replicated between the source system and the replica system.
  • the merkle tree on the replica side is updated as blocks of data are written to cached data structures and/or storage. Accordingly, the merkle tree is being updated on the replica system in a similar way as the merkle tree was updated on the source side. In one embodiment, the top level node of the merkle tree is compared.
  • the top two, three, etc., layers of the merkle tree are compared.
  • the source side and the replica side must be in sync in regard to any data that is to be written.
  • the replica side must also handle that write prior to the verification step. In one embodiment, this is accomplished through messaging between the source and replica systems.
  • the replica server can send a message requesting verification data.
  • the source system can pause handling write requests until the verification data, e.g., the merkle tree nodes, are sent to the replica side.
  • the replica side receiving the verification data handles any queued write requests prior to comparing the received verification data with local data.
  • the replica system can send a message and the I/O can continue.
  • the replica side can queue any received I/O requests from the source side. This allows the source side to begin handling I/O as soon as the verification data has been sent to the replica system. Once the verification is done, the replica system can handle any queued I/O requests. Verification can be done at any point during the replication process. The only requirement is that the source and replica side be in sync in regard to handling write requests. For example, after a certain number of blocks have been replicated or after a predetermined amount of time has passed, the replica server can request verification data from the source system.
  • Replication data between different systems can impact the performance of both systems.
  • Quality of service can be implemented on both the source system and the replica system to ensure adequate service is provided based upon quality of service provisions.
  • Embodiments of quality of service provisions that can be used in replication are described in U.S. application Ser. No. 13/856,958, which is incorporated by reference in its entirety.
  • the quality of service allocated for I/O for a particular volume can be different on the source system compared to the replica system. For example, the replica system may have allocated 1,000 input output per second (IOPs), while the source system has allocated 5,000 IOPs for a particular volume. In this situation, the source system could overload the replica system's ability to handle the IOPs associated with replicating the volume from the source system to the replica system.
  • IOPs input output per second
  • the handling of I/O can be paused.
  • a timer can be used to monitor how long I/O has been paused. If the timer exceeds some threshold, the replication of the source volume can be stopped and reported.
  • volumes that are to be replicated can be sorted based upon quality of service (QoS) parameters associated with the volumes.
  • QoS quality of service
  • sorting is done on the sum of QoS parameters from the source system and the replica system. This sum can represent a relative importance of a volume, with higher QoS parameters being more important than lower level QoS parameter volumes.
  • the ratio of the replica QoS parameter to the source QoS parameter is used to sort the volumes. Volumes with higher ratios indicate that the replication of those volumes are likely to successfully finish. Volumes whose ratios fall below a threshold amount can be flagged as volumes whose replication may not successfully finish due to QoS provisions.
  • the source side's QoS provisions could force the replica side to throttle I/O to the point that the replica side terminates the replication as described above.
  • the volumes can be sorted based upon the replica system's QoS parameter only. This allows volumes to be given high replication priority by increasing the QoS provisions of the volume on the replica server, without having to modify the source side's QoS provisions. Accordingly, a replication of a volume can be assured to successfully complete based upon a high QoS parameter on the replica side.
  • the volumes can be sorted based upon the source system's QoS parameter only. Once the volumes have been sorted, replication can begin in an ordered fashion based upon the sorting.
  • Warnings can be generated for any volume that is below some threshold, e.g., ratio below a threshold, sum is below a threshold, etc.
  • the warnings can provide information regarding the replication and the QoS parameters, such that the QoS parameters can be modified to remove future warnings.
  • any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being “operably couplable” to each other to achieve the desired functionality.
  • operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.

Abstract

Disclosed are systems, computer-readable mediums, and methods for receiving a start replication message to replicate a source volume to a replicated volume. A source system forwards I/O requests to the replica server. A data structure associated with the replicated volume is initialized. A write request is received from the source system. The write data is written to the replicated volume and the data structure is updated. Source metadata associated with the source volume is received. The source metadata is compared with prior metadata associated with a prior point-in-time image of the source volume to determine blocks of data that have changed since the prior point-in-time image of the source volume. A first block is determined to not be retrieved based upon the data structure. A second block is determined to be retrieved based upon the data structure. The second block is received and written to the replicated volume.

Description

    BACKGROUND
  • The following description is provided to assist the understanding of the reader. None of the information provided is admitted to be prior art.
  • In data storage architectures, a client's data may be stored in a volume. A unit of data, for example a file (or object), is comprised of one or more storage units (e.g. bytes) and can be stored and retrieved from a storage medium such as disk or RAM in a variety of fashions. For example, disk drives in storage systems are divided into logical blocks that are addressed using logical block addresses (LBAs). As another example, an entire file can be stored in a contiguous range of addresses on the storage medium and be accessed given the offset and length of the file. Most modern file systems store files by dividing them into blocks or extents of a fixed size, storing each block in a contiguous section of the storage medium, and then maintaining a list or tree of the blocks that correspond to each file. Some storage systems, such as write-anywhere file layout (WAFL), logical volume manager (LVM), or new technology file system (NTFS), allow multiple objects to refer to the same blocks, typically through a tree structure, to allow for efficient storage of previous versions or “snapshots” of the file system. In some cases, data for a single file or object may be distributed between multiple storage devices, either by a mechanism like RAID which combines several smaller storage media into one larger virtual device, or through a distributed storage system such as Lustre, General Parallel File System, or GlusterFS.
  • At some point, it is desirable to backup data of the storage system. Traditional backup methods typically utilize backup software that operates independently of the data storage system and manages the backup process. Backup methods exist to backup only the differences since the last full backup (e.g., a differential backup) or to backup only the changes since the last backup (e.g., an incremental backup). However, due to inefficiency of backup software, many administrators are shifting away from traditional backup processes and moving towards data replication methods. With replication comes the issue of replicating a mistake, for example, a wrongly deleted file. High bandwidth is required for both replication and backup solutions, and neither methods are particularly well suited to scale efficiently for long term archiving.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • The foregoing and other features of the present disclosure will become more fully apparent from the following description and appended claims, taken in conjunction with the accompanying drawings.
  • FIG. 1 depicts a simplified system for a storage system in accordance with an illustrative implementation.
  • FIG. 2A depicts a hash tree in accordance with an illustrative implementation.
  • FIG. 2B depicts the hash tree illustrated in FIG. 2A, with updated node hashes, in accordance with an illustrative implementation.
  • FIG. 2C depicts the hash tree illustrated in FIG. 2A, with newly added leaves, in accordance with an illustrative implementation.
  • FIG. 3 shows a flow diagram of an incremental block level backup procedure in accordance with an illustrative implementation.
  • FIG. 4 depicts a distributed storage system in accordance with an illustrative implementation.
  • FIG. 5 shows a flow diagram for replicating data in accordance with an illustrative implementation.
  • DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS Overview
  • In general, one innovative aspect of the subject matter described below can be embodied in methods for receiving a start replication message from a source system to replicate data of a source volume to a replicated volume on a replica server. The replicated volume comprises a copy of data of the source volume. The source system forwards input/output (I/O) requests to the replica server after the start replication message is sent. A data structure associated with units of data of the replicated volume is initialized. A write request is received from the source system that includes write data associated a unit of data of the replicated volume. The source system wrote the write data to the source volume based upon the write request. The write data is written to the replicated volume. The data structure is updated to indicate the write data has been written after the receipt of the start replication message. Source metadata associated with the source volume is received. The metadata includes an ordered list of block identifiers for data blocks of the source volume. Each block identifier is used to access a data block. The source metadata is compared with prior metadata associated with a prior point-in-time image of the source volume to determine blocks of data that have changed since the prior point-in-time image of the source volume. A first block of the blocks of data is determined to not be retrieved based upon the data structure. A second block of the blocks of data is determined to be retrieved based upon the data structure. The second block is received from the source system and written to the replicated volume. Other embodiments of this aspect include corresponding systems, apparatuses, and computer-readable media, configured to perform the actions of the method.
  • The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, implementations, and features described above, further aspects, implementations, and features will become apparent by reference to the following drawings and the detailed description.
  • DETAILED DESCRIPTION
  • Described herein are techniques for an incremental block level backup system. In the following description, for purposes of explanation, numerous examples and specific details are set forth in order to provide a thorough understanding of various implementations. Particular implementations as defined by the claims may include some or all of the features in these examples alone or in combination with other features described below, and may further include modifications and equivalents of the features and concepts described herein.
  • Storage System
  • FIG. 1 depicts a simplified system for incremental block level backup of a storage system 100 in accordance with an illustrative implementation. System 100 includes a client layer 102, a metadata layer 104, a block server layer 106, storage 116, and storage 120.
  • In general, client layer 102 includes one or more clients 108 a-108 n. Clients 108 include client processes that may exist on one or more physical machines. When the term “client” is used in the disclosure, the action being performed may be performed by a client process. A client process is responsible for storing, retrieving, and deleting data in system 100. A client process may address pieces of data depending on the nature of the storage system and the format of the data stored. For example, the client process may reference data using a client address. The client address may take different forms. For example, in a storage system that uses file storage, client 108 may reference a particular volume or partition, and a file name. With object storage, the client address may be a unique object name. For block storage, the client address may be a volume or partition, and a block address. Clients 108 communicate with metadata layer 104 using different protocols, such as small computer system interface (SCSI), Internet small computer system interface (ISCSI), fibre channel (FC), common Internet file system (CIFS), network file system (NFS), hypertext transfer protocol (HTTP), hypertext transfer protocol secure (HTTPS), web-based distributed authoring and versioning (WebDAV), or a custom protocol.
  • Metadata layer 104 includes one or more metadata servers 110 a-110 n. Performance managers 114 may be located on metadata servers 110 a-110 n. Block server layer 106 includes one or more block servers 112 a-112 n. Block servers 112 a-112 n are coupled to storage 116, which stores volume data for clients 108. Each client 108 may be associated with a volume. In one implementation, only one client 108 accesses data in a volume; however, multiple clients 108 may access data in a single volume.
  • Storage 116 can include multiple solid state drives (SSDs). In one implementation, storage 116 can be a cluster of individual drives coupled together via a network. When the term “cluster” is used, it will be recognized that cluster may represent a storage system that includes multiple disks that may not be networked together. In one implementation, storage 116 uses solid state memory to store persistent data. SSDs use microchips that store data in non-volatile memory chips and contain no moving parts. One consequence of this is that SSDs allow random access to data in different drives in an optimized manner as compared to drives with spinning disks. Read or write requests to non-sequential portions of SSDs can be performed in a comparable amount of time as compared to sequential read or write requests. In contrast, if spinning disks were used, random read/writes would not be efficient since inserting a read/write head at various random locations to read data results in slower data access than if the data is read from sequential locations. Accordingly, using electromechanical disk storage can require that a client's volume of data be concentrated in a small relatively sequential portion of the cluster to avoid slower data access to non-sequential data. Using SSDs removes this limitation.
  • In various implementations, non-sequentially storing data in storage 116 is based upon breaking data up into one more storage units, e.g., data blocks. A data block, therefore, is the raw data for a volume and may be the smallest addressable unit of data. The metadata layer 104 or the client layer 102 can break data into data blocks. The data blocks can then be stored on multiple block servers 112. Data blocks can be of a fixed size, can be initially a fixed size but compressed, or can be of a variable size. Data blocks can also be segmented based on the contextual content of the block. For example, data of a particular type may have a larger data block size compared to other types of data. Maintaining segmentation of the blocks on a write (and corresponding re-assembly on a read) may occur in client layer 102 and/or metadata layer 104. Also, compression may occur in client layer 102, metadata layer 104, and/or block server layer 106.
  • In addition to storing data non-sequentially, data blocks can be stored to achieve substantially even distribution across the storage system. In various examples, even distribution can be based upon a unique block identifier. A block identifier can be an identifier that is determined based on the content of the data block, such as by a hash of the content. The block identifier is unique to that block of data. For example, blocks with the same content have the same block identifier, but blocks with different content have different block identifiers. To achieve even distribution, the values of possible unique identifiers can have a uniform distribution. Accordingly, storing data blocks based upon the unique identifier, or a portion of the unique identifier, results in the data being stored substantially evenly across drives in the cluster.
  • Because client data, e.g., a volume associated with the client, is spread evenly across all of the drives in the cluster, every drive in the cluster is involved in the read and write paths of each volume. This configuration balances the data and load across all of the drives. This arrangement also removes hot spots within the cluster, which can occur when client's data is stored sequentially on any volume.
  • In addition, having data spread evenly across drives in the cluster allows a consistent total aggregate performance of a cluster to be defined and achieved. This aggregation can be achieved, since data for each client is spread evenly through the drives. Accordingly, a client's I/O will involve all the drives in the cluster. Since, all clients have their data spread substantially evenly through all the drives in the storage system, a performance of the system can be described in aggregate as a single number, e.g., the sum of performance of all the drives in the storage system.
  • Block servers 112 and slice servers maintain a mapping between a block identifier and the location of the data block in a storage medium of block server 112. A volume includes these unique and uniformly random identifiers, and so a volume's data is also evenly distributed throughout the cluster.
  • Metadata layer 104 stores metadata that maps between client layer 102 and block server layer 106. For example, metadata servers 110 map between the client addressing used by clients 108 (e.g., file names, object names, block numbers, etc.) and block layer addressing (e.g., block identifiers) used in block server layer 106. Clients 108 may perform access based on client addresses. However, as described above, block servers 112 store data based upon identifiers and do not store data based on client addresses. Accordingly, a client can access data using a client address which is eventually translated into the corresponding unique identifiers that reference the client's data in storage 116.
  • Although the parts of system 100 are shown as being logically separate, entities may be combined in different fashions. For example, the functions of any of the layers may be combined into a single process or single machine (e.g., a computing device) and multiple functions or all functions may exist on one machine or across multiple machines. Also, when operating across multiple machines, the machines may communicate using a network interface, such as a local area network (LAN) or a wide area network (WAN). In one implementation, one or more metadata servers 110 may be combined with one or more block servers 112 or backup servers 118 in a single machine. Entities in system 100 may be virtualized entities. For example, multiple virtual block servers 112 may be included on a machine. Entities may also be included in a cluster, where computing resources of the cluster are virtualized such that the computing resources appear as a single entity.
  • Block Level Incremental Backup
  • One or more backup servers 118 a-118 n can interface with the metadata layer 104. Backup servers 118 can interface directly with block servers 112. Backup servers 118 a-118 n are coupled to storage 120, which stores backups of volume data for clients 108. Storage 120 can include multiple hard disk drives (HDDs), solid state drives (SSDs), hybrid drives, or other storage drives. In one implementation, storage 120 can be a cluster of individual drives coupled together via a network. Backup servers 118 can store backup copies of the data blocks of storage 116 according to any number of formats in storage 120, and translation from the format of the data blocks of storage 116 may occur. Data may be transferred to and from backup servers 118 using different protocols, such as small computer system interface (SCSI), Internet small computer system interface (ISCSI), fibre channel (FC), common Internet file system (CIFS), network file system (NFS), hypertext transfer protocol (HTTP), hypertext transfer protocol secure (HTTPS), web-based distributed authoring and versioning (WebDAV), or a custom protocol. Compression and data de-duplication may occur in backup servers 118 a-118 n.
  • As discussed above, the servers of metadata layer 104 store and maintain metadata that maps between client layer 102 and block server layer 106, where the metadata maps between the client addressing used by clients 108 (e.g., file names, volume, object names, block numbers, etc.) and block layer addressing (e.g., block identifiers) used in block server layer 106. In one embodiment, the metadata includes a list of block identifiers that identifies blocks in a volume. The list may be structured as an ordered list corresponding to a list of blocks. The list may also be structured as the leaves of a hash tree. The block identifiers of the metadata are the same block identifiers as used throughout system 100 as described above. The block identifiers may be hexadecimal numbers, but other representations may be used. Additional metadata may also be included, such as inode numbers, directory pointers, modification dates, file size, client addresses, list details, etc. The block identifiers uniquely identify the data of a block and are a hash based on the content of the data block. Backup servers 118 are generally configured to create backups of block level data of a volume that is stored in storage 116 of block server layer 106. Backup servers 118 may create backups of all of the volume data of block server layer 106 or backup servers 118 may create backups of one or more particular volumes (e.g., a volume of a client 108). Backups may be full backups of all data, or they may be incremental backups (e.g., data that has changed since a previous backup).
  • During an initial backup operation, a backup server 118 retrieves a copy of metadata from metadata server 110 for a client volume. The metadata includes a list of block identifiers associated with data blocks of the volume. In an implementation, the metadata includes an ordered list structure of block identifiers. In another implementation, the ordered list is structured as the leaves of a hash tree (e.g., a Merkle tree, etc.) and the metadata includes the hash tree. The metadata is used by backup server 118 to retrieve a copy of all of the data blocks of the client volume in order to create an initial backup of the data blocks. The data blocks are retrieved from storage 116 by sending a request for the data to a metadata server 110. The requested data is based on the data block identifiers. A request may include a list of the block identifiers of blocks desired to be backed up. In one implementation, backup server 118 may calculate the LBAs of blocks desired to be backed up. For example, because each block identifier can represent a known amount of data (e.g., a 4 k block, etc.), an LBA of a block can be calculated based on the location of the block identifier in the ordered list of block identifiers associated with the volume. For example, the position of a block identifier in the ordered list can be used along with the block size to determine the LBA of the data block. As described below, the tree structure can also be used to determine the data blocks that have changed after a previous backup. In this example, the number of leaf nodes to the left of a changed leaf node can be used to calculate the LBA of the data block. In implementations where LBAs are calculated, a request from backup server 118 may include a list of LBAs of blocks to be backed up. The metadata server 110 routes the request to a block server 112, which provides the requested data to metadata server 110. Metadata server 110 then routes the requested data to the backup server 118. This arrangement allows the servers of metadata layer 104 to facilitate data transmission between block server layer 106 and the backup servers 118. In another implementation, backup servers 118 may be configured to communicate directly with servers of block server layer 106. Upon retrieval of the requested data, the backup server 118 stores the data in storage 120. The data may be stored in storage 120 according to any of the methods discussed herein. Backup server 118 may create and maintain statistics and snapshot data corresponding to a particular backup operation. The snapshot data may be used later during a data restoration operation, or during a future backup operation. Backup server 118 can also store a copy of the metadata used during a particular backup operation. In another embodiment, the metadata is not stored on the backup server 118. Rather, the metadata is stored on another storage device, for example, one or more metadata servers, one or more block servers, or one or more devices remote from the backup system. As a result of the initial backup operation, a complete backup of the data of a client volume is created and stored in storage 120.
  • During an incremental backup operation, a backup server 118 retrieves the current metadata from metadata server 110 for a client volume. The backup server 118 can then compare the current metadata from metadata server 110 with a version of stored metadata on backup server 118 (e.g., the version of metadata stored during the most recent backup operation, or the initial version of the metadata stored during the initial backup, etc.). In an implementation where the metadata includes an ordered list of block identifiers, the backup server 118 can compare the block identifiers of the two versions of metadata node-by-node. For example, the current list node corresponding to a first block of data is compared to the stored list node corresponding to the first block of data, and each node of the ordered list is traversed and compared. Since the block identifiers are hashes based on content of a corresponding data block, a difference in hash values for corresponding nodes indicates that the data of the block has been changed/updated since the prior backup. As the block identifiers are integral to storage system 100 and maintained as described herein, the block identifiers can be compared in their native format and immediately used without the need to compute the hash values. In an implementation where the metadata includes a hash tree and the ordered list of block identifiers are structured as the leaves of the hash tree, additional performance gains may be realized. Such a hash tree is generally a tree data structure in which every non-leaf node includes the hash of its children nodes. This structure is particularly useful because it allows efficient determination of which data blocks have been updated since a prior backup, without the need to compare every node of the list of block identifiers. The determination of changed data blocks by using a hash tree will be discussed in further detail below with reference to FIGS. 2 a-b. Upon determination of which particular blocks of data have changed since the previous backup, backup server 118 can retrieve the updated blocks of data from storage 116 by sending a request for the changed data block to the metadata server 110. As discussed above, the metadata server 110 can facilitate the transfer of data from the block server layer 106. Upon retrieval of the requested changed data blocks, the backup server 118 stores the data in storage 120. The backup server 118 also stores the current metadata from metadata server 110 used in the incremental backup operation. As a result of the incremental backup operation, only the data of a volume that has changed since a previous backup operation is backed up again. This provides a number of advantages, including increasing the efficiency of the data backup procedure, and decreasing the overall amount of data being transferred during the backup procedure. Further, any number of incremental backup operations may be performed, during which the current metadata from metadata server 110 may be compared to previously stored metadata on backup server 118 (e.g., the stored metadata from a prior backup operation).
  • Backup servers 118 may also provide an application programming interface (API) in order to allow clients 108 or traditional data backup software to interface with the backup systems described herein. For example, the API may allow backup servers 118 to send statistics related to backed up data and backup operations to and from clients 108 or traditional backup software. As another example, the API may allow backup servers 118 to receive a request to initiate a backup operation. The API can also allow for backup operations to be scheduled as desired by clients 108 or as controlled by data backup software. Other API functionality is also envisioned.
  • Referring to FIG. 2 a, a hash tree 200 a is shown in accordance with an illustrative implementation. The hash tree 200 a may be a hash tree that is provided by a metadata server 110 to a backup server 118 in an initial or incremental backup operation as discussed above. Although depicted as a binary hash tree, hash tree 200 a (and hash trees described herein) may have any number of child nodes/branches. Hash tree 200 a represents the data of a particular volume, and can be provided along with additional metadata describing details related to the tree structure. For example, the metadata may include statistics regarding node counts, leaf-node counts, tree-depth, indexes to sub-trees, etc. Backup server 118 may store the additional metadata for future use. Hash tree 200 a includes leaves 202 a-d, internal nodes 204 a-b, and root node 206. Leaves 202 a-d store block identifies B1-B4, respectively. In an implementation, leaves 202 a-d may be structured as an ordered list that is indexed by its parent nodes, which in this example are internal nodes 204. Block identifiers B1-B4 are identifiers as described herein (e.g., a hash of the corresponding data block's content), and each uniquely identify a particular data block of the volume. Hash tree 200 a further includes non-leaf internal nodes 204 a-b and non-leaf root node 206. The value stored by each non-leaf node is the hash of that node's children values. For example, hash H1 is the hash of block identifiers B1 and B2, hash H2 is the hash of block identifiers B3 and B4, and hash H3 is the hash of hashes H1 and H2. During an initial backup operation, backup server 118 can walk the tree, or traverse the ordered list of leaves 202 a-d to determine that the data blocks corresponding to block identifiers B1-B4 should be retrieved to be backed up. A copy of hash tree 200 a (and any accompanying metadata) is stored by backup server 118 when a backup operation is performed.
  • Referring to FIG. 2 b, the hash tree 200 a of FIG. 2 a is shown at a later time instance, as hash tree 200 b. For example, hash tree 200 a may have been provided by metadata server 110 during an initial backup operation and stored by the backup server 118, and hash tree 200 b may have been provided by metadata server 110 during a subsequent incremental backup operation. Both hash trees 200 a-b represent the data stored on a particular volume. As depicted, the block identifier B3 of leaf node 202 c has changed to become block identifier B3′ at some time since the previous backup. For example, new or updated data may have been written to the block referenced by block identifier B3. Because of the structure of the hash tree, the change of block identifier from B3 to B3′ causes updates in hashes to propagate upward through the parent node to the root node. Specifically, hash H2 is recalculated to become H2′, and hash H3 is recalculated to become to H3′. During a backup operation, backup server 118 may walk the hash tree 200 b, and compare the nodes of hash tree 200 b to corresponding nodes of hash tree 200 a. A difference between corresponding non-leaf node hashes indicates that a block identifier (and therefore block data) below that non-leaf node has changed. If the hashes of corresponding non-leaf nodes are equal, this indicates that the block identifiers below that non-leaf node have not changed (and therefore corresponding block data has also not changed). Thus, the subtree of nodes below an unchanged non-leaf node can be skipped from further processing. In this manner, a performance increase may be realized as the entire hash tree does not need to be traversed in every backup operation. As an example with reference to FIG. 2 b, backup server 118 may compare hash tree 200 b to hash tree 200 a as follows (although analysis performed by backup server 118 is not limited to the following operations or order of operations):
      • 1. Node 206 is analyzed to determine that hash H3′ is different from its previous value of H3, and therefore hash trees 200 a-b need to be further analyzed.
      • 2. Node 204 a is analyzed to determine that hash H1 has not changed, and the subtree of node 204 a (leaf nodes 202 a-b) may be skipped from further analysis.
      • 3. Node 204 b is analyzed to determine that hash H2′ is different from its previous value of H2, therefore the subtree of node 204 b (leaf nodes 202 c-d) must be analyzed.
      • 4. Leaf node 202 c is analyzed to determine that block identifier B3′ is different from its previous value of B3. Thus, the data block corresponding to block identifier B3′ needs to be backed up by backup server 118, since its data as changed since the previous backup operation.
      • 5. Leaf node 202 d is analyzed to determine that block identifier B4 has not changed, and traversal of hash trees 200 a-b is complete.
  • After performing the above sample analysis, backup server 118 may proceed to retrieve the data based on the block identifier(s) that indicate data has changed, and has not yet been backed up. In this example, backup server 118 may send a request to a metadata server 110 for the data block identified by block identifier B3′. Upon receipt of the data block, backup server 118 stores the data block as a backup, and stores hash tree 200 b (along with any accompanying metadata) for use in future backup and/or restoration operations.
  • In one implementation using trees, backup server 118 may retrieve the metadata from a metadata server 110 by requesting only child nodes whose parent node has changed. For example, starting with the root, if the root node has changed the children of the root node can then be requested. These nodes can then be compared to corresponding nodes in the previously stored tree to determine if those have changed. Children of any node that has changed can then be retrieved. This process can be repeated until leaf nodes are retrieved. For example, with reference to FIGS. 2A-B hash tree 200 b may be the current metadata from metadata server 110, and hash tree 200 a may be stored metadata from a previous backup operation. Backup server 118 may first retrieve root node 206 and analyze it to determine that hash H3′ is different from its previous value of H3. In response, backup server 118 may then request nodes 204 a-b from interior node level 204. Node 204 a is analyzed to determine that hash H1 has not changed, and leaf nodes 202 a-b may be skipped from further requests/analysis. Node 204 b is analyzed to determine that hash H2′ is different from its previous value of H2, and thus backup server 118 may proceed to request appropriate nodes of leaf level 202 (leaves 202 c-d). Analysis may then continue as described above to determine that block identifier B3′ is different from its previous value of B3 and that the data block corresponding to block identifier B3′ needs to be backed up. This implementation may allow for performance increases by minimizing data that is transmitted between backup server 118 and metadata server 110 during the retrieval of metadata.
  • At some point, it may be desirable by clients 108 or an administrator of system 100 to increase the volume size assigned to a client 108 by adding more data blocks of storage space. In this situation, with backup servers 118 implementations configured to utilize metadata of an ordered list of block identifiers, any newly added block identifiers (corresponding to the new data blocks) may be appended to the end of the ordered list. Thus, during a backup operation, if a backup server 118 receives metadata of an ordered list that has more elements than that of metadata from a prior backup operation, backup server 118 can determine the newly added data blocks that must be backed up based on the additional list elements. The backup operation may proceed as described above with respect to the remaining elements.
  • FIG. 2C depicts the result of an increased volume size for implementations configured to utilize metadata of a hash tree. Hash tree 200 c is based on hash tree 200 a (which is included as a subtree and is denoted by a dashed box). Leaves 202 e-f have been newly added to the hash tree and include block identifiers B5-B6, which correspond to the newly added data blocks of the increased volume size. As a result of the volume increase, hash tree 200 a is restructured such that root node 206 becomes internal node 206 a, and a new root node 208 is created. Further, internal nodes 206 b and 204 c are added to maintain the tree structure. Hashes H4-H6 are calculated based on the respective child values as described above. After such a restructuring of a hash tree, a backup operation may proceed as described above. However, backup server 118 can determine the newly added data blocks that must be backed up based on a new root node or additional leaves. Also, an implementation may make use of additional metadata that includes the indexes of the root nodes of previously stored trees. In this manner, backup server 118 may access the indexes to locate and compare the root node of a prior tree with the corresponding internal node of the current tree (e.g., root node 206 can be compared to internal node 206 a.). If the comparison indicates that the hashes have not changed, then backup server 118 may skip analyzing the subtree of the internal node, and a performance gain may be realized.
  • At some point, it may be desirable by clients 108 or an administrator of system 100 to reduce the volume size assigned to a client 108 by removing data blocks of storage space. In this situation, with backup server 118 implementations configured to utilize metadata of an ordered list of block identifiers, any removed block identifiers (corresponding to removed data blocks) may be removed from the end of the ordered list. Thus, during a backup operation, if a backup server 118 receives metadata of an ordered list that has fewer elements than that of metadata from a prior backup operation, backup server 118 can determine the backed up data blocks that may be removed based on the additional list elements in the stored list from the prior backup. The backup operation may proceed as described above with respect to the remaining elements. With backup server 118 implementations configured to utilize metadata of a hash tree including leaves that are a list of block identifiers, the backup server 118 may compare the trees (e.g. depth of the trees, leaf node count, etc.) to determine that there has been a change in volume size. In another implementation the size of the volume can be part of the metadata received by the backup servers, and this metadata can be compared to a previously received volume size to determine that a change in volume has occurred. The backup server may then determine the position of the current tree within the stored hash tree. After locating the position of the current root node, the leaf nodes (and corresponding parent nodes) that are not within the subtree of the current root node can be ignored. Once the corresponding root nodes have been determined, the backup operation may then proceed as described above with respect to the remaining nodes.
  • FIG. 3 shows a simplified flow diagram of an incremental block level backup procedure 300, in accordance with an embodiment. Additional, fewer, or different operations of the procedure 300 may be performed, depending on the particular embodiment. The procedure 300 can be implemented on a computing device. In one implementation, the procedure 300 is encoded on a computer-readable medium that contains instructions that, when executed by a computing device, cause the computing device to perform operations of the procedure 300. According to different embodiments, at least a portion of the various types of functions, operations, actions, and/or other features provided by the incremental block level backup procedure may be implemented at one or more nodes and/or volumes of the storage system. In an operation 302, metadata for a particular volume is retrieved (e.g., from a metadata server). For example, a backup sever may initiate a backup operation and retrieve initial metadata as described above. In an alternative embodiment, the backup server may be responding to a request to initiate a backup operation. For example, a client or backup software may submit a request via an API to perform a backup at a certain time. Alternatively, the backup server may be performing a backup according to a schedule (e.g., nightly backups, weekly backups, client-specified backups, etc.). In an operation 304, the initial backup of the data blocks of the volume is created. The metadata provides the block identifiers corresponding to the volume. The metadata may include an ordered list of block identifiers, a hash tree based on block identifiers, and other related data. The block identifiers are used to retrieve the corresponding data blocks to be backed up. For example, the backup server may analyze the metadata in order to request the transmission of and retrieve particular data blocks to be backed up. The request may be sent to the metadata server, which can facilitate the transmission of data from a block server. In an alternative embodiment, the backup server may retrieve the data blocks directly from the block server. The initial backup is a backup of all of the data of the volume as specified by the metadata. In an operation 306, the metadata used for the initial backup is stored for future use. In an operation 308, an incremental backup of the volume is initiated by retrieving the current metadata. For example, sometime after the creation of the initial backup, the backup server may retrieve updated metadata, which has been maintained by the metadata server to be current with the data blocks of the volume. As another example, metadata may be retrieved from a remote storage device. In an operation 310, the current metadata is compared to other metadata (e.g., the metadata from the immediately preceding backup operation, the metadata from the initial backup operation, the metadata from a remote device, etc.). For example, the backup server may analyze the metadata to determine changes in block identifiers as discussed above. Based on any changed block identifiers found during the analysis, in an operation 312, an incremental backup is created. For example, based on the identifiers of the changed data blocks, the backup server may retrieve only the changed data blocks to be backed up. The backup server may store received data blocks as described herein. In an operation 314, the metadata used for the incremental backup is stored for future use. The backup server may also generate additional metadata related to the backup procedure, including statistics to the amount of data backed up, the elapsed time of the backup process, etc. This process may repeat any number of times to create any number of incremental backups, as indicated by operation 316.
  • In another embodiment, the retrieval of the metadata and the comparison of the metadata to other metadata is performed by a device other than the backup server (e.g., by one or more devices of the storage system). For example, a storage device remote from the backup server may access metadata on the storage device, or may retrieve the metadata from another device, for example, from the metadata server. The storage device may analyze the metadata to determine changes in block identifiers as discussed above. Based on any changed block identifiers found during the analysis, an incremental backup can be created by transferring data to the backup server. For example, based on the identifiers of the changed data blocks, the storage device may transfer only the changed data blocks to the backup server to be backed up. The backup server may store received data blocks as described herein. The metadata used for the incremental backup can be stored by the storage device or can be transferred to another device (e.g., the metadata server) to be stored for future use.
  • Data Syncing in a Distributed System
  • In various embodiments, data can synced/replicated to another location. For example, data from a source system can be copied to a replica server. Data can be replicated locally, to another volume in its cluster, to another cluster, to a remote storage device, etc. Data that can be replicated includes, but is not limited to, block server data, metadata server data, etc. Replicated data is a representation of the data on the source system at a particular point in time. To reduce impact on the source system during replication, the replication process does not stop incoming I/O operations. To allow I/O operations to continue during a replication, writes that occur during the replication must be properly handled to avoid mismatches in data between the live data and the corresponding replicated data.
  • FIG. 4 depicts a distributed storage system 400 in accordance with an illustrative implementation. The storage system 400 stores live client data and may be configured as discussed above regarding system 100 (e.g., including client layer 102, metadata layer 104, block server layer 106, and storage). The storage system 400 can also include one or more replica servers 418 a-418 n. Replica servers 418 a-418 n can interface with the metadata and/or block servers of the storage system 400 in order to maintain synchronized (replicated) copies of data stored by the storage system 400. Replica servers 418 a-418 n are coupled to storage 420, which may store backups of volume data (e.g., backups of block level data of a client volume), synchronized data of client volume, snapshots of a client volume, and associated metadata. Storage 420 may include multiple hard disk drives (HDDs), solid state drives (SSDs), hybrid drives, or other storage drives. In one implementation, storage 420 can be a cluster of individual drives coupled together via a network. Replica servers 418 can store backup copies of the data blocks of storage system 400 according to any number of formats in storage 420, and translation from the format of the data blocks may occur.
  • In one embodiment, a replica server 418 maintains a live synchronized copy of data blocks of a client volume (e.g., a mirror copy of the client volume). To maintain synchronization, requests to write data that are provided by a client to storage system 400 may also be transmitted to the replica server 418. In this manner, data written to storage system 400 can be synchronized and stored on replica server 418 in real-time or semi real-time. Synchronization of volume data on replica server 418 includes synchronizing the metadata of storage system 400 that identifies blocks in a client volume. As discussed above, metadata servers of the storage system store metadata that includes a list of block identifiers that identifies blocks in a volume. The block identifiers may be hexadecimal numbers, and other representations may be used. Additional metadata may also be included (e.g., inode numbers, directory pointers, modification dates, file size, client addresses, list details, etc.). The block identifiers uniquely identify the data of a block and are a hash based on the content of the data block. In an embodiment, the metadata includes an ordered list structure of block identifiers. In another embodiment, the ordered list is structured as the leaves of a hash tree (e.g., a Merkle tree, etc.) and the metadata includes the hash tree. In an implementation utilizing a tree, when a write request is received and data is written to a block of a volume, values of the leaves (and inner nodes) of the tree change to corresponding to the changes of the block. Thus, replica server 418 can maintain a live synchronization tree that is updated to parallel the a tree maintained by a metadata server of storage system 400 for a particular client volume.
  • FIG. 5 shows a flow diagram for replicating data in accordance with an illustrative implementation. Replication begins with a replica server receiving a start replication message from a source system (502). Upon receipt of the start replication message, the replica server initiates a data structure that will be used to track writes that occur during the replication process (504). In one embodiment, the data structure is a bit field where each bit represents a single unit of information, e.g., a block, a sub-block, etc. Each bit in the bit field represents if a particular unit has been written to after the start of the replication processes. In this embodiment, the bit field will be initialized to 0. At some point after sending the start replication message, the source system sends over replication data to the replica server. Similar to the block level backup embodiments, merkle trees can be used to minimize the amount of data that is required to be transferred between the source system and the replica server.
  • While the replication data is being sent to the replica server, data writes can be received at the source system. For example, a user may be writing new data to a file or metadata related to a user volume could be updated. The source system will handle the writes and while the replication process is active will also send the writes to the replica server. For example, the replica server can receive an I/O request to write a block of data (550). Upon receipt, the replica server can write the block of data (552) and will also update the bit associated with the block in the bit field to 1 (554). After the bit is set, the data write on the replica server is complete.
  • As part of the replication process, the replica server determines which blocks of data are needed from the source system (506). For example, a merkle tree comparison as described above can be used to determine blocks of data that have changed since a previous point-in-time image. One or more of the changed blocks of data, however, may have been changed again since the start of the replication process. Accordingly, the data will have already been sent to the replica server and requesting this data again is unneeded. Before requesting the block of data from source system, the bit field can be checked to determine if the block has already been received (508). If the block has not been updated, then the block of data is requested from the source system (510). The block is received (512) and written to storage. If the block has been updated, then no request for that block of data needs to be sent to the source system. This continues until there are no longer any data blocks that are needed from the source system. Once there are no longer any data blocks, the volume has been replicated. The replication system can send a message to the source system indicating that replication is complete. Upon receipt, the source system can stop forwarding I/O to the replication system.
  • In one embodiment, a block is the smallest amount of data that is written to storage in a single write operation. A block, however, can be divided into smaller sub-blocks, such that each unit of a block can be written to separately. As an example, a block can be 4 kilobytes in size and broken down into sixteen 256 byte sub-blocks. In this embodiment, the data structure corresponds to the sub-blocks and not the blocks. While replication is being done, a write to a sub-block can be received. The write command can include the data for the entire block or just the sub-block of data. The write can update a cache that is associated with the sub-block or could write the sub-block to storage. When only a sub-block is received in the write request, the block that contains the sub-block is retrieved and the sub-block is updated appropriately. Later during replication, the Merkle tree comparison can be used to determine that the block with the updated sub-block needs to be retrieved from the source system. For example, another sub-block may have been update from the previous replication. The entire block can be retrieved. The corresponding block on the replica server is retrieved and updated. To update the corresponding block on the replica server, the data structure is used to update each sub-block from the block retrieved from the source system. For sub-blocks where the data structure indicates that the sub-block has been updated during the replication process, the sub-block is not updated since it already has the latest data. If the data structure indicates that a sub-block has not been updated, that sub-block is updated with the corresponding sub-block received from the source system. To reduce unnecessary data transfers, before the replica server requests a block, the replica server can determine if all the sub-blocks of a block have been updated during the replica process. In this case, the replica server has already replicated this block and there is no need to request that block of data from the source system.
  • As described above, replica servers 418 a-418 n can be configured to create point-in-time images of components of the data of storage system 400. In one embodiment, each point-in-time image includes corresponding metadata (e.g., a hash tree) that identifies the blocks of the point-in-time image. The hash tree of a point-in-time image is based on the block identifiers of the data stored for the point-in-time image. A replica server 418 may create one or more point-in-time images of a component of the data of storage system 400, and each point-in-time image may be created according a defined schedule, or on demand (e.g., in response to a client demand, or as demanded by an administrator of storage system 400, etc.). The source system may also create various copies/replicas of a volume locally. For example, every day a replica of a volume can be scheduled. A remote replication system may only replicate a subset of the replicas that are local to the source system. For example, a remote replication system can request a single local copy every week rather than each of the daily local replicas. In another embodiment, the remote replication system can make a replica of the current live volume and ignore any other local replicas of the volume.
  • In the instance that a replica server 418 goes offline (e.g., due to a failure, being manually taken offline, or otherwise), the replica server 418 may be brought back online and resume synchronizing volume data with storage system 400. However, due to the period of time that the replica server 418 was offline, the data of replica server 418 may be out of sync with the volume data of storage system 400. Accordingly, replica server 418 may retrieve the data that is needed from storage system 400 to re-synchronize with the live volume data of storage system 400. In one embodiment, replica server 418 may implement one or more techniques of the block level incremental backup process to synchronize the volume data. For example, replica server 418 can retrieve the metadata for a live volume (e.g., a tree corresponding to the live volume as maintained by a metadata server). Replica server 418 may then analyze versions of metadata (e.g., comparing the out-of-date synchronization tree of replica server 418 and the retrieved live volume tree). Based on this analysis, replica server 418 can determine changed data blocks of the volume and what blocks needs to be retrieved from storage system 400 to synchronize the volume data. The replica server 418 may request any changed data blocks from storage system 400 and the retrieved blocks may be stored. As replica server 418 is synchronizing its volume data, write requests may still be received and the point-in-time image can still be created. In the instance that a new point-in-time image is being created and the volume data of replica server 418 is not fully synchronized with the live volume data of storage system 400, a data block may not yet be available in the data of replica server 418 to be stored in the new point-in-time image. For example, referring to the new point-in-time image creation process discussed above, the comparison of the metadata of the new tree with the metadata of the live tree may indicate that a block identifier (and therefore block data) has changed. However, the changed block may not yet be synchronized in the volume data of replica server 418. In this scenario, replica server 418 may retrieve the changed block data directly from the storage system 400 (as opposed to pointing to or retrieving the changed block data from the synchronized volume data of replica server 418 as discussed above).
  • After replication of a volume has completed, the replication can be verified. In one embodiment, this is done by the source system sending to the replica system one or more merkle tree nodes. The replica system can then compare the received merkle tree nodes with the corresponding merkle tree nodes of the replicated copy of the source volume. If any corresponding nodes do not match, the data was not properly replicated between the source system and the replica system. In this embodiment, the merkle tree on the replica side is updated as blocks of data are written to cached data structures and/or storage. Accordingly, the merkle tree is being updated on the replica system in a similar way as the merkle tree was updated on the source side. In one embodiment, the top level node of the merkle tree is compared. In other embodiments, the top two, three, etc., layers of the merkle tree are compared. For this comparison to work properly, the source side and the replica side must be in sync in regard to any data that is to be written. For example, if data is written on the source side, the replica side must also handle that write prior to the verification step. In one embodiment, this is accomplished through messaging between the source and replica systems. Once the replication is complete, the replica server can send a message requesting verification data. The source system can pause handling write requests until the verification data, e.g., the merkle tree nodes, are sent to the replica side. The replica side receiving the verification data handles any queued write requests prior to comparing the received verification data with local data. Once verification is done, the replica system can send a message and the I/O can continue. In another embodiment, the replica side can queue any received I/O requests from the source side. This allows the source side to begin handling I/O as soon as the verification data has been sent to the replica system. Once the verification is done, the replica system can handle any queued I/O requests. Verification can be done at any point during the replication process. The only requirement is that the source and replica side be in sync in regard to handling write requests. For example, after a certain number of blocks have been replicated or after a predetermined amount of time has passed, the replica server can request verification data from the source system.
  • Replication data between different systems can impact the performance of both systems. Quality of service can be implemented on both the source system and the replica system to ensure adequate service is provided based upon quality of service provisions. Embodiments of quality of service provisions that can be used in replication are described in U.S. application Ser. No. 13/856,958, which is incorporated by reference in its entirety. The quality of service allocated for I/O for a particular volume can be different on the source system compared to the replica system. For example, the replica system may have allocated 1,000 input output per second (IOPs), while the source system has allocated 5,000 IOPs for a particular volume. In this situation, the source system could overload the replica system's ability to handle the IOPs associated with replicating the volume from the source system to the replica system. Once the IOPs threshold has been reached on the replica system, the handling of I/O can be paused. A timer can be used to monitor how long I/O has been paused. If the timer exceeds some threshold, the replication of the source volume can be stopped and reported.
  • To reduce replications from being stopped, volumes that are to be replicated can be sorted based upon quality of service (QoS) parameters associated with the volumes. In one embodiment, sorting is done on the sum of QoS parameters from the source system and the replica system. This sum can represent a relative importance of a volume, with higher QoS parameters being more important than lower level QoS parameter volumes. In another embodiment, the ratio of the replica QoS parameter to the source QoS parameter is used to sort the volumes. Volumes with higher ratios indicate that the replication of those volumes are likely to successfully finish. Volumes whose ratios fall below a threshold amount can be flagged as volumes whose replication may not successfully finish due to QoS provisions. For example, if the ratio is less than one, the source side's QoS provisions could force the replica side to throttle I/O to the point that the replica side terminates the replication as described above. In another embodiment, the volumes can be sorted based upon the replica system's QoS parameter only. This allows volumes to be given high replication priority by increasing the QoS provisions of the volume on the replica server, without having to modify the source side's QoS provisions. Accordingly, a replication of a volume can be assured to successfully complete based upon a high QoS parameter on the replica side. In another embodiment, the volumes can be sorted based upon the source system's QoS parameter only. Once the volumes have been sorted, replication can begin in an ordered fashion based upon the sorting. Warnings can be generated for any volume that is below some threshold, e.g., ratio below a threshold, sum is below a threshold, etc. The warnings can provide information regarding the replication and the QoS parameters, such that the QoS parameters can be modified to remove future warnings.
  • One or more flow diagrams have been used herein. The use of flow diagrams is not meant to be limiting with respect to the order of operations performed. The herein-described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being “operably couplable” to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.
  • With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.
  • It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”
  • The foregoing description of illustrative implementations has been presented for purposes of illustration and of description. It is not intended to be exhaustive or limiting with respect to the precise form disclosed, and modifications and variations are possible in light of the above teachings or may be acquired from practice of the disclosed implementations. It is intended that the scope of the invention be defined by the claims appended hereto and their equivalents.

Claims (20)

What is claimed is:
1. A system comprising:
a replica server comprising one or more processors configured to:
receive a start replication message from a source system to replicate data of a source volume to a replicated volume on the replica server, wherein the replicated volume comprises a copy of data of the source volume, and wherein the source system forwards input/output (I/O) requests to the replica server after the start replication message is sent;
initiate a data structure associated with units of data of the replicated volume;
receive, from the source system, a write request comprising write data associated a unit of data of the replicated volume, wherein the source system wrote the write data to the source volume based upon the write request;
write the write data to the replicated volume;
update the data structure to indicate the write data has been written after the receipt of the start replication message;
receive source metadata associated with the source volume, wherein the metadata comprises an ordered list of block identifiers for data blocks of the source volume, and wherein each block identifier is used to access a data block;
compare the source metadata with prior metadata associated with a prior point-in-time image of the source volume to determine blocks of data that have changed since the prior point-in-time image of the source volume;
determine a first block of the blocks of data should not be retrieved based upon the data structure;
determine a second block of the blocks of data should be retrieved based upon the data structure;
retrieve the second block from the source system; and
write the second block to the replicated volume.
2. The system of claim 1, wherein the one or more processors are further configured to send a replication complete message to the source system, wherein the source system no longer forwards I/O to the replica server based upon receipt of the replication complete message.
3. The system of claim 1, wherein the one or more processors are further configured to:
request verification data from the source system;
receive the verification data from the source system; and
compare the received verification data with corresponding verification data of the replica volume to determine the replication was successful.
4. The system of claim 1, wherein a unit of data is a sub-block, wherein a block consists of a plurality of sub-blocks, and wherein to write the second block the one or more processors are further configured to:
determine which sub-blocks have been updated since the receipt of the start replication message based upon the data structure; and
write sub-blocks of the second block based upon on the sub-block not being updated since the receipt of the start replication message.
5. The system of claim 1, further comprising a data server, wherein the data server comprising one or more processors configured to:
request quality of service parameters for the replicated volume;
request quality of service parameters for the source volume;
determine if the replication of the source volume to the replicated volume will succeed based upon the quality of service parameters of the replicated volume and the source volume.
6. The system of claim 5, wherein the data server comprising one or more processors are further configured to determine a ratio of the quality of service parameters for the replicated volume to the quality of service parameters for the source volume, wherein the replication of the source volume will succeed if the ratio is greater than one.
7. The system of claim 1, wherein the data blocks of the replicated volume are randomly and evenly distributed across a cluster containing the replicated volume.
8. A method comprising:
receiving, at a replica server, a start replication message from a source system to replicate data of a source volume to a replicated volume on the replica server, wherein the replicated volume comprises a copy of data of the source volume, and wherein the source system forwards input/output (I/O) requests to the replica server after the start replication message is sent;
initiating a data structure associated with units of data of the replicated volume;
receiving, from the source system, a write request comprising write data associated a unit of data of the replicated volume, wherein the source system wrote the write data to the source volume based upon the write request;
writing the write data to the replicated volume;
updating the data structure to indicate the write data has been written after the receipt of the start replication message;
receiving source metadata associated with the source volume, wherein the metadata comprises an ordered list of block identifiers for data blocks of the source volume, and wherein each block identifier is used to access a data block;
comparing the source metadata with prior metadata associated with a prior point-in-time image of the source volume to determine blocks of data that have changed since the prior point-in-time image of the source volume;
determining, using a processor, a first block of the blocks of data should not be retrieved based upon the data structure;
determining a second block of the blocks of data should be retrieved based upon the data structure;
retrieving the second block from the source system; and
writing the second block to the replicated volume.
9. The method of claim 8, further comprising sending a replication complete message to the source system, wherein the source system no longer forwards I/O to the replica server based upon receipt of the replication complete message.
10. The method of claim 8, further comprising:
requesting verification data from the source system;
receiving the verification data from the source system; and
comparing the received verification data with corresponding verification data of the replica volume to determine the replication was successful.
11. The method of claim 8, wherein a unit of data is a sub-block, wherein a block consists of a plurality of sub-blocks, and wherein the method further comprises:
determining which sub-blocks have been updated since the receipt of the start replication message based upon the data structure; and
writing sub-blocks of the second block based upon on the sub-block not being updated since the receipt of the start replication message.
12. The method of claim 8, further comprising
requesting quality of service parameters for the replicated volume;
requesting quality of service parameters for the source volume;
determining if the replication of the source volume to the replicated volume will succeed based upon the quality of service parameters of the replicated volume and the source volume.
13. The method of claim 12, further comprising determining a ratio of the quality of service parameters for the replicated volume to the quality of service parameters for the source volume, wherein the replication of the source volume will succeed if the ratio is greater than one.
14. The method of claim 8, wherein the data blocks of the replicated volume are randomly and evenly distributed across a cluster containing the replicated volume.
15. A non-transitory computer-readable medium having instructions stored thereon, the instructions comprising:
instructions to receive a start replication message from a source system to replicate data of a source volume to a replicated volume on the replica server, wherein the replicated volume comprises a copy of data of the source volume, and wherein the source system forwards input/output (I/O) requests to the replica server after the start replication message is sent;
instructions to initiate a data structure associated with units of data of the replicated volume;
instructions to receive, from the source system, a write request comprising write data associated a unit of data of the replicated volume, wherein the source system wrote the write data to the source volume based upon the write request;
instructions to write the write data to the replicated volume;
instructions to update the data structure to indicate the write data has been written after the receipt of the start replication message;
instructions to receive source metadata associated with the source volume, wherein the metadata comprises an ordered list of block identifiers for data blocks of the source volume, and wherein each block identifier is used to access a data block;
instructions to compare the source metadata with prior metadata associated with a prior point-in-time image of the source volume to determine blocks of data that have changed since the prior point-in-time image of the source volume;
instructions to determine a first block of the blocks of data should not be retrieved based upon the data structure;
instructions to determine a second block of the blocks of data should be retrieved based upon the data structure;
instructions to retrieve the second block from the source system; and
instructions to write the second block to the replicated volume.
16. The non-transitory computer-readable medium of claim 15, wherein the instructions further comprise instructions to send a replication complete message to the source system, wherein the source system no longer forwards I/O to the replica server based upon receipt of the replication complete message.
17. The non-transitory computer-readable medium of claim 15, wherein the instructions further comprise instructions to:
instructions to request verification data from the source system;
instructions to receive the verification data from the source system; and
instructions to compare the received verification data with corresponding verification data of the replica volume to determine the replication was successful.
18. The non-transitory computer-readable medium of claim 15, wherein a unit of data is a sub-block, wherein a block consists of a plurality of sub-blocks, and wherein the instructions to write the second block comprise:
instructions to determine which sub-blocks have been updated since the receipt of the start replication message based upon the data structure; and
instructions to write sub-blocks of the second block based upon on the sub-block not being updated since the receipt of the start replication message.
19. The non-transitory computer-readable medium of claim 15, wherein the instructions further comprise instructions to:
instructions to request quality of service parameters for the replicated volume;
instructions to request quality of service parameters for the source volume;
instructions to determine if the replication of the source volume to the replicated volume will succeed based upon the quality of service parameters of the replicated volume and the source volume.
20. The non-transitory computer-readable medium of claim 15, wherein the data blocks of the replicated volume are randomly and evenly distributed across a cluster containing the replicated volume.
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Cited By (92)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160110261A1 (en) * 2013-05-07 2016-04-21 Axcient, Inc. Cloud storage using merkle trees
US20160154704A1 (en) * 2014-11-27 2016-06-02 Institute For Information Industry Backup system and backup method thereof
US20170060432A1 (en) * 2015-08-28 2017-03-02 Vmware, Inc. Scalable storage space allocation in distributed storage systems
US9613046B1 (en) * 2015-12-14 2017-04-04 Netapp, Inc. Parallel optimized remote synchronization of active block storage
US9671960B2 (en) 2014-09-12 2017-06-06 Netapp, Inc. Rate matching technique for balancing segment cleaning and I/O workload
US20170177445A1 (en) * 2015-12-18 2017-06-22 Dropbox, Inc. Network folder resynchronization
US9710317B2 (en) 2015-03-30 2017-07-18 Netapp, Inc. Methods to identify, handle and recover from suspect SSDS in a clustered flash array
US9720601B2 (en) 2015-02-11 2017-08-01 Netapp, Inc. Load balancing technique for a storage array
US9740566B2 (en) 2015-07-31 2017-08-22 Netapp, Inc. Snapshot creation workflow
US9762460B2 (en) 2015-03-24 2017-09-12 Netapp, Inc. Providing continuous context for operational information of a storage system
US20170295239A1 (en) * 2014-12-27 2017-10-12 Huawei Technologies Co.,Ltd. Data processing method, apparatus, and system
US20170300424A1 (en) * 2014-10-01 2017-10-19 Cacheio Llc Efficient metadata in a storage system
US9798728B2 (en) 2014-07-24 2017-10-24 Netapp, Inc. System performing data deduplication using a dense tree data structure
US9836229B2 (en) 2014-11-18 2017-12-05 Netapp, Inc. N-way merge technique for updating volume metadata in a storage I/O stack
US9923762B1 (en) * 2013-08-13 2018-03-20 Ca, Inc. Upgrading an engine when a scenario is running
US10127243B2 (en) * 2015-09-22 2018-11-13 International Business Machines Corporation Fast recovery using self-describing replica files in a distributed storage system
US10133511B2 (en) 2014-09-12 2018-11-20 Netapp, Inc Optimized segment cleaning technique
US10324903B1 (en) * 2017-12-28 2019-06-18 Dropbox, Inc. Content management client synchronization service
US10331362B1 (en) * 2016-09-30 2019-06-25 EMC IP Holding Company LLC Adaptive replication for segmentation anchoring type
US20190197562A1 (en) * 2017-12-27 2019-06-27 Irene Woerner System and method for product authentication
CN110730228A (en) * 2019-10-10 2020-01-24 深圳市网心科技有限公司 Data storage method, electronic device, system and medium
US20200073962A1 (en) * 2018-08-29 2020-03-05 International Business Machines Corporation Checkpointing for increasing efficiency of a blockchain
US10776355B1 (en) 2016-09-26 2020-09-15 Splunk Inc. Managing, storing, and caching query results and partial query results for combination with additional query results
US10826795B2 (en) 2014-05-05 2020-11-03 Nutanix, Inc. Architecture for implementing service level management for a virtualization environment
US10911328B2 (en) 2011-12-27 2021-02-02 Netapp, Inc. Quality of service policy based load adaption
US10929022B2 (en) 2016-04-25 2021-02-23 Netapp. Inc. Space savings reporting for storage system supporting snapshot and clones
US10949309B2 (en) * 2015-12-28 2021-03-16 Netapp Inc. Snapshot creation with synchronous replication
US10951488B2 (en) 2011-12-27 2021-03-16 Netapp, Inc. Rule-based performance class access management for storage cluster performance guarantees
US10949387B1 (en) * 2016-09-29 2021-03-16 Triad National Security, Llc Scalable filesystem enumeration and metadata operations
US10951465B1 (en) * 2016-09-29 2021-03-16 Emc Ïp Holding Company Llc Distributed file system analytics
US10956415B2 (en) 2016-09-26 2021-03-23 Splunk Inc. Generating a subquery for an external data system using a configuration file
US10977260B2 (en) * 2016-09-26 2021-04-13 Splunk Inc. Task distribution in an execution node of a distributed execution environment
CN112685378A (en) * 2019-10-17 2021-04-20 伊姆西Ip控股有限责任公司 Method, apparatus and computer-readable storage medium for garbage collection
US10984044B1 (en) 2016-09-26 2021-04-20 Splunk Inc. Identifying buckets for query execution using a catalog of buckets stored in a remote shared storage system
US10997098B2 (en) 2016-09-20 2021-05-04 Netapp, Inc. Quality of service policy sets
US11003714B1 (en) 2016-09-26 2021-05-11 Splunk Inc. Search node and bucket identification using a search node catalog and a data store catalog
US11010351B1 (en) * 2018-10-31 2021-05-18 EMC IP Holding Company LLC File system replication between software defined network attached storage processes using file system snapshots
US11010435B2 (en) 2016-09-26 2021-05-18 Splunk Inc. Search service for a data fabric system
US11023463B2 (en) 2016-09-26 2021-06-01 Splunk Inc. Converting and modifying a subquery for an external data system
US11106645B1 (en) * 2015-09-29 2021-08-31 EMC IP Holding Company LLC Multi point in time object store
US11106734B1 (en) 2016-09-26 2021-08-31 Splunk Inc. Query execution using containerized state-free search nodes in a containerized scalable environment
US11126632B2 (en) 2016-09-26 2021-09-21 Splunk Inc. Subquery generation based on search configuration data from an external data system
US11144401B2 (en) * 2018-11-16 2021-10-12 Vmware, Inc. Component aware incremental backup, restore, and reconciliation solution
US11151137B2 (en) 2017-09-25 2021-10-19 Splunk Inc. Multi-partition operation in combination operations
US11163758B2 (en) 2016-09-26 2021-11-02 Splunk Inc. External dataset capability compensation
US11196542B2 (en) 2018-08-29 2021-12-07 International Business Machines Corporation Checkpointing for increasing efficiency of a blockchain
US11216204B2 (en) * 2019-11-19 2022-01-04 Netapp, Inc. Degraded redundant metadata, DRuM, technique
US11222066B1 (en) 2016-09-26 2022-01-11 Splunk Inc. Processing data using containerized state-free indexing nodes in a containerized scalable environment
US11226985B2 (en) * 2015-12-15 2022-01-18 Microsoft Technology Licensing, Llc Replication of structured data records among partitioned data storage spaces
US11232100B2 (en) 2016-09-26 2022-01-25 Splunk Inc. Resource allocation for multiple datasets
US11243963B2 (en) 2016-09-26 2022-02-08 Splunk Inc. Distributing partial results to worker nodes from an external data system
US20220043799A1 (en) * 2020-08-07 2022-02-10 EMC IP Holding Company LLC Method, device, and computer program product for metadata comparison
US11250056B1 (en) 2016-09-26 2022-02-15 Splunk Inc. Updating a location marker of an ingestion buffer based on storing buckets in a shared storage system
US11269939B1 (en) 2016-09-26 2022-03-08 Splunk Inc. Iterative message-based data processing including streaming analytics
US11281706B2 (en) 2016-09-26 2022-03-22 Splunk Inc. Multi-layer partition allocation for query execution
US11294941B1 (en) 2016-09-26 2022-04-05 Splunk Inc. Message-based data ingestion to a data intake and query system
US11314753B2 (en) 2016-09-26 2022-04-26 Splunk Inc. Execution of a query received from a data intake and query system
US11321321B2 (en) 2016-09-26 2022-05-03 Splunk Inc. Record expansion and reduction based on a processing task in a data intake and query system
US11334543B1 (en) 2018-04-30 2022-05-17 Splunk Inc. Scalable bucket merging for a data intake and query system
US11334439B2 (en) 2018-08-29 2022-05-17 International Business Machines Corporation Checkpointing for increasing efficiency of a blockchain
US11379119B2 (en) 2010-03-05 2022-07-05 Netapp, Inc. Writing data in a distributed data storage system
US11386120B2 (en) 2014-02-21 2022-07-12 Netapp, Inc. Data syncing in a distributed system
US11442935B2 (en) 2016-09-26 2022-09-13 Splunk Inc. Determining a record generation estimate of a processing task
US11461334B2 (en) 2016-09-26 2022-10-04 Splunk Inc. Data conditioning for dataset destination
US11494380B2 (en) 2019-10-18 2022-11-08 Splunk Inc. Management of distributed computing framework components in a data fabric service system
US11500875B2 (en) 2017-09-25 2022-11-15 Splunk Inc. Multi-partitioning for combination operations
US20220391359A1 (en) * 2021-06-07 2022-12-08 Netapp, Inc. Distributed File System that Provides Scalability and Resiliency
US11550847B1 (en) 2016-09-26 2023-01-10 Splunk Inc. Hashing bucket identifiers to identify search nodes for efficient query execution
US11562023B1 (en) 2016-09-26 2023-01-24 Splunk Inc. Merging buckets in a data intake and query system
US11567993B1 (en) 2016-09-26 2023-01-31 Splunk Inc. Copying buckets from a remote shared storage system to memory associated with a search node for query execution
US11579978B2 (en) 2018-02-14 2023-02-14 Rubrik, Inc. Fileset partitioning for data storage and management
US11580107B2 (en) 2016-09-26 2023-02-14 Splunk Inc. Bucket data distribution for exporting data to worker nodes
US11586692B2 (en) 2016-09-26 2023-02-21 Splunk Inc. Streaming data processing
US11586627B2 (en) 2016-09-26 2023-02-21 Splunk Inc. Partitioning and reducing records at ingest of a worker node
US11593377B2 (en) 2016-09-26 2023-02-28 Splunk Inc. Assigning processing tasks in a data intake and query system
US11599541B2 (en) 2016-09-26 2023-03-07 Splunk Inc. Determining records generated by a processing task of a query
US11604795B2 (en) 2016-09-26 2023-03-14 Splunk Inc. Distributing partial results from an external data system between worker nodes
US11615087B2 (en) 2019-04-29 2023-03-28 Splunk Inc. Search time estimate in a data intake and query system
US11615104B2 (en) 2016-09-26 2023-03-28 Splunk Inc. Subquery generation based on a data ingest estimate of an external data system
US11620191B2 (en) * 2018-10-01 2023-04-04 Rubrik, Inc. Fileset passthrough using data management and storage node
US11620336B1 (en) 2016-09-26 2023-04-04 Splunk Inc. Managing and storing buckets to a remote shared storage system based on a collective bucket size
US11663227B2 (en) 2016-09-26 2023-05-30 Splunk Inc. Generating a subquery for a distinct data intake and query system
US11669428B2 (en) 2020-05-19 2023-06-06 Paypal, Inc. Detection of matching datasets using encode values
US11704313B1 (en) 2020-10-19 2023-07-18 Splunk Inc. Parallel branch operation using intermediary nodes
US11715051B1 (en) 2019-04-30 2023-08-01 Splunk Inc. Service provider instance recommendations using machine-learned classifications and reconciliation
US11797565B2 (en) * 2019-12-30 2023-10-24 Paypal, Inc. Data validation using encode values
US11835990B2 (en) 2021-11-16 2023-12-05 Netapp, Inc. Use of cluster-level redundancy within a cluster of a distributed storage management system to address node-level errors
US11860940B1 (en) 2016-09-26 2024-01-02 Splunk Inc. Identifying buckets for query execution using a catalog of buckets
US11868656B2 (en) 2021-06-07 2024-01-09 Netapp, Inc. Distributed file system with disaggregated data management and storage management layers
US11874691B1 (en) 2016-09-26 2024-01-16 Splunk Inc. Managing efficient query execution including mapping of buckets to search nodes
US11921672B2 (en) 2017-07-31 2024-03-05 Splunk Inc. Query execution at a remote heterogeneous data store of a data fabric service
US11922222B1 (en) 2020-01-30 2024-03-05 Splunk Inc. Generating a modified component for a data intake and query system using an isolated execution environment image

Families Citing this family (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10747753B2 (en) 2015-08-28 2020-08-18 Swirlds, Inc. Methods and apparatus for a distributed database within a network
US9529923B1 (en) 2015-08-28 2016-12-27 Swirlds, Inc. Methods and apparatus for a distributed database within a network
US9390154B1 (en) 2015-08-28 2016-07-12 Swirlds, Inc. Methods and apparatus for a distributed database within a network
US11386067B2 (en) 2015-12-15 2022-07-12 Red Hat, Inc. Data integrity checking in a distributed filesystem using object versioning
CN105740048B (en) * 2016-01-26 2019-03-08 华为技术有限公司 A kind of mirror image management method, apparatus and system
US10176216B2 (en) 2016-02-01 2019-01-08 International Business Machines Corporation Verifying data consistency
WO2017156184A1 (en) 2016-03-09 2017-09-14 Alibaba Group Holding Limited Cross-regional data transmission
SG11201903278YA (en) 2016-11-10 2019-05-30 Swirlds Inc Methods and apparatus for a distributed database including anonymous entries
RU2754189C2 (en) 2016-12-19 2021-08-30 Свирлдз, Инк. Method and device for distributed database that allows deleting events
US10521344B1 (en) * 2017-03-10 2019-12-31 Pure Storage, Inc. Servicing input/output (‘I/O’) operations directed to a dataset that is synchronized across a plurality of storage systems
US10282115B2 (en) 2017-04-13 2019-05-07 International Business Machines Corporation Object synchronization in a clustered system
KR102348418B1 (en) 2017-07-11 2022-01-07 스월즈, 인크. Methods and apparatus for efficiently implementing a distributed database within a network
RU2740865C1 (en) 2017-11-01 2021-01-21 Свирлдз, Инк. Methods and device for efficient implementation of database supporting fast copying
US11120133B2 (en) * 2017-11-07 2021-09-14 Spinbackup Inc. Ransomware protection for cloud storage systems
US10671370B2 (en) * 2018-05-30 2020-06-02 Red Hat, Inc. Distributing file system states
US10922312B2 (en) * 2018-09-24 2021-02-16 International Business Machines Corporation Optimization of data processing job execution using hash trees
KR20200119601A (en) * 2019-04-10 2020-10-20 현대모비스 주식회사 Apparatus and method for secure update of a binary data in vehicle
CN113711202A (en) * 2019-05-22 2021-11-26 斯沃尔德斯股份有限公司 Method and apparatus for implementing state attestation and ledger identifiers in a distributed database
US11366879B2 (en) * 2019-07-08 2022-06-21 Microsoft Technology Licensing, Llc Server-side audio rendering licensing
CN110442430B (en) * 2019-08-06 2021-11-19 上海浦东发展银行股份有限公司信用卡中心 Publishing method based on distributed storage container cloud application
CN112905691A (en) * 2019-11-19 2021-06-04 中盈优创资讯科技有限公司 Information synchronization method and device
US11386122B2 (en) * 2019-12-13 2022-07-12 EMC IP Holding Company LLC Self healing fast sync any point in time replication systems using augmented Merkle trees
US11928085B2 (en) 2019-12-13 2024-03-12 EMC IP Holding Company LLC Using merkle trees in any point in time replication
US11461183B2 (en) * 2020-01-08 2022-10-04 EMC IP Holding Company LLC Trivial snapshots
US11461363B2 (en) * 2020-03-31 2022-10-04 Sap Se Memory allocation and deallocation mechanism to reduce fragmentation and enhance defragmentation performance
US20230125637A1 (en) * 2021-10-27 2023-04-27 Microsoft Technology Licensing, Llc Enhanced co-authoring and file syncing
US20230185670A1 (en) * 2021-12-13 2023-06-15 Scality, S.A. Method and apparatus for monitoring storage system replication

Citations (34)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6779003B1 (en) * 1999-12-16 2004-08-17 Livevault Corporation Systems and methods for backing up data files
US20050027817A1 (en) * 2003-07-31 2005-02-03 Microsoft Corporation Replication protocol for data stores
US7188149B2 (en) * 2001-03-07 2007-03-06 Hitachi, Ltd. Storage operating data control system
US20070083482A1 (en) * 2005-10-08 2007-04-12 Unmesh Rathi Multiple quality of service file system
US20070088702A1 (en) * 2005-10-03 2007-04-19 Fridella Stephen A Intelligent network client for multi-protocol namespace redirection
US20070186127A1 (en) * 2006-02-03 2007-08-09 Emc Corporation Verification of computer backup data
US20070186066A1 (en) * 2006-02-03 2007-08-09 Emc Corporation Fast verification of computer backup data
US20070208918A1 (en) * 2006-03-01 2007-09-06 Kenneth Harbin Method and apparatus for providing virtual machine backup
US7395283B1 (en) * 2003-11-10 2008-07-01 Emc Corporation Method and apparatus for making independent data copies in a data processing system
US7543100B2 (en) * 2001-06-18 2009-06-02 3Par, Inc. Node controller for a data storage system
US20090157870A1 (en) * 2005-09-20 2009-06-18 Nec Corporation Resource-amount calculation system, and method and program thereof
US20090271412A1 (en) * 2008-04-29 2009-10-29 Maxiscale, Inc. Peer-to-Peer Redundant File Server System and Methods
US7805583B1 (en) * 2003-04-23 2010-09-28 Emc Corporation Method and apparatus for migrating data in a clustered computer system environment
US7817562B1 (en) * 2006-09-29 2010-10-19 Emc Corporation Methods and systems for back end characterization using I/O sampling
US7962709B2 (en) * 2005-12-19 2011-06-14 Commvault Systems, Inc. Network redirector systems and methods for performing data replication
US8055745B2 (en) * 2004-06-01 2011-11-08 Inmage Systems, Inc. Methods and apparatus for accessing data from a primary data storage system for secondary storage
US20120003940A1 (en) * 2009-03-25 2012-01-05 Nec Corporation Communication device, recording medium for control program of communication device, communication system and communication method
US8122213B2 (en) * 2009-05-05 2012-02-21 Dell Products L.P. System and method for migration of data
US20120317353A1 (en) * 2011-06-13 2012-12-13 XtremlO Ltd. Replication techniques with content addressable storage
US20130007097A1 (en) * 2011-06-30 2013-01-03 Hitachi, Ltd. Server system and method for controlling information system
US20130073519A1 (en) * 2011-09-20 2013-03-21 Netapp, Inc. Handling data extent size asymmetry during logical replication in a storage system
US20130138616A1 (en) * 2011-11-29 2013-05-30 International Business Machines Corporation Synchronizing updates across cluster filesystems
US20130185719A1 (en) * 2012-01-17 2013-07-18 Microsoft Corporation Throttling guest write ios based on destination throughput
US20130232261A1 (en) * 2011-12-27 2013-09-05 Solidfire, Inc. Quality of service policy sets
US8555019B2 (en) * 2010-09-08 2013-10-08 International Business Machines Corporation Using a migration cache to cache tracks during migration
US20140006353A1 (en) * 2012-06-28 2014-01-02 International Business Machines Corporation Recording backup information for backed-up data items in a data item list
US8671265B2 (en) * 2010-03-05 2014-03-11 Solidfire, Inc. Distributed data storage system providing de-duplication of data using block identifiers
US20140108350A1 (en) * 2011-09-23 2014-04-17 Hybrid Logic Ltd System for live-migration and automated recovery of applications in a distributed system
US20140310231A1 (en) * 2013-04-16 2014-10-16 Cognizant Technology Solutions India Pvt. Ltd. System and method for automating data warehousing processes
US20140344222A1 (en) * 2013-05-16 2014-11-20 Oracle International Corporation Method and apparatus for replication size estimation and progress monitoring
US20150066852A1 (en) * 2013-08-27 2015-03-05 Netapp, Inc. Detecting out-of-band (oob) changes when replicating a source file system using an in-line system
US9047211B2 (en) * 2013-03-15 2015-06-02 SanDisk Technologies, Inc. Managing data reliability
US9092142B2 (en) * 2012-06-26 2015-07-28 Hitachi, Ltd. Storage system and method of controlling the same
US9411620B2 (en) * 2010-11-29 2016-08-09 Huawei Technologies Co., Ltd. Virtual storage migration method, virtual storage migration system and virtual machine monitor

Family Cites Families (744)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH01175671A (en) 1987-12-29 1989-07-12 Nippon Steel Corp Method for converting drawing data
US5375216A (en) 1992-02-28 1994-12-20 Motorola, Inc. Apparatus and method for optimizing performance of a cache memory in a data processing system
US5459857A (en) 1992-05-15 1995-10-17 Storage Technology Corporation Fault tolerant disk array data storage subsystem
JP3188071B2 (en) 1993-10-14 2001-07-16 富士通株式会社 Disk cache device
US5603001A (en) 1994-05-09 1997-02-11 Kabushiki Kaisha Toshiba Semiconductor disk system having a plurality of flash memories
JP2507235B2 (en) 1994-06-24 1996-06-12 インターナショナル・ビジネス・マシーンズ・コーポレイション Client server computer system, its client computer, server computer, and object updating method
US5542089A (en) 1994-07-26 1996-07-30 International Business Machines Corporation Method and apparatus for estimating the number of occurrences of frequent values in a data set
US5864698A (en) 1994-08-24 1999-01-26 Packard Bell Nec Disk based bios
US5511190A (en) 1995-01-20 1996-04-23 Tandem Computers, Inc. Hash-based database grouping system and method
US5611073A (en) 1995-02-09 1997-03-11 Delco Electronics Corp. Method of ensuring parameter coherency in a multi-processor system
US5666512A (en) 1995-02-10 1997-09-09 Hewlett-Packard Company Disk array having hot spare resources and methods for using hot spare resources to store user data
US5751993A (en) 1995-09-05 1998-05-12 Emc Corporation Cache management system
US5592432A (en) 1995-09-05 1997-01-07 Emc Corp Cache management system using time stamping for replacement queue
US5860082A (en) 1996-03-28 1999-01-12 Datalight, Inc. Method and apparatus for allocating storage in a flash memory
US5974421A (en) 1996-12-23 1999-10-26 Microsoft Corporation Cache-efficient object loader
US5991862A (en) 1996-12-30 1999-11-23 Sun Microsystems, Inc. Modified indirect addressing for file system
US6257756B1 (en) 1997-07-16 2001-07-10 Motorola, Inc. Apparatus and method for implementing viterbi butterflies
US6067541A (en) 1997-09-17 2000-05-23 Microsoft Corporation Monitoring document changes in a file system of documents with the document change information stored in a persistent log
US5937425A (en) 1997-10-16 1999-08-10 M-Systems Flash Disk Pioneers Ltd. Flash file system optimized for page-mode flash technologies
US5890161A (en) 1997-10-28 1999-03-30 Microsoft Corporation Automatic transaction processing of component-based server applications
JP4033310B2 (en) 1997-12-16 2008-01-16 富士通株式会社 Auxiliary storage device for information equipment and information equipment
US6493811B1 (en) 1998-01-26 2002-12-10 Computer Associated Think, Inc. Intelligent controller accessed through addressable virtual space
US6047283A (en) 1998-02-26 2000-04-04 Sap Aktiengesellschaft Fast string searching and indexing using a search tree having a plurality of linked nodes
US6385699B1 (en) 1998-04-10 2002-05-07 International Business Machines Corporation Managing an object store based on object replacement penalties and reference probabilities
US6219800B1 (en) 1998-06-19 2001-04-17 At&T Corp. Fault-tolerant storage system
US6560196B1 (en) 1998-11-19 2003-05-06 Cisco Technology, Inc. Method and apparatus for controlling the transmission of cells across a network
US8266266B2 (en) 1998-12-08 2012-09-11 Nomadix, Inc. Systems and methods for providing dynamic network authorization, authentication and accounting
US7194554B1 (en) 1998-12-08 2007-03-20 Nomadix, Inc. Systems and methods for providing dynamic network authorization authentication and accounting
US6347337B1 (en) 1999-01-08 2002-02-12 Intel Corporation Credit based flow control scheme over virtual interface architecture for system area networks
US6795890B1 (en) 1999-02-19 2004-09-21 Mitsubishi Denki Kabushiki Kaisha Data storage method, and data processing device using an erasure block buffer and write buffer for writing and erasing data in memory
US6397307B2 (en) 1999-02-23 2002-05-28 Legato Systems, Inc. Method and system for mirroring and archiving mass storage
US7210001B2 (en) 1999-03-03 2007-04-24 Adaptec, Inc. Methods of and apparatus for efficient buffer cache utilization
US6081900A (en) 1999-03-16 2000-06-27 Novell, Inc. Secure intranet access
US6275898B1 (en) 1999-05-13 2001-08-14 Lsi Logic Corporation Methods and structure for RAID level migration within a logical unit
US6876668B1 (en) 1999-05-24 2005-04-05 Cisco Technology, Inc. Apparatus and methods for dynamic bandwidth allocation
US6553384B1 (en) 1999-06-14 2003-04-22 International Business Machines Corporation Transactional name service
US6363385B1 (en) 1999-06-29 2002-03-26 Emc Corporation Method and apparatus for making independent data copies in a data processing system
AU6104800A (en) 1999-07-16 2001-02-05 Intertrust Technologies Corp. Trusted storage systems and methods
US6785704B1 (en) 1999-12-20 2004-08-31 Fastforward Networks Content distribution system for operation over an internetwork including content peering arrangements
US6721789B1 (en) 1999-10-06 2004-04-13 Sun Microsystems, Inc. Scheduling storage accesses for rate-guaranteed and non-rate-guaranteed requests
US6578158B1 (en) 1999-10-28 2003-06-10 International Business Machines Corporation Method and apparatus for providing a raid controller having transparent failover and failback
US6434662B1 (en) 1999-11-02 2002-08-13 Juniper Networks, Inc. System and method for searching an associative memory utilizing first and second hash functions
US6604155B1 (en) 1999-11-09 2003-08-05 Sun Microsystems, Inc. Storage architecture employing a transfer node to achieve scalable performance
US6728843B1 (en) 1999-11-30 2004-04-27 Hewlett-Packard Development Company L.P. System and method for tracking and processing parallel coherent memory accesses
US6609176B1 (en) 1999-12-27 2003-08-19 Kabushiki Kaisha Toshiba Disk control system and data rearrangement method
US6434555B1 (en) 2000-01-24 2002-08-13 Hewlett Packard Company Method for transaction recovery in three-tier applications
US6741698B1 (en) 2000-01-27 2004-05-25 Avaya Technology Corp. Call management system using dynamic threshold adjustment
US6526478B1 (en) 2000-02-02 2003-02-25 Lsi Logic Corporation Raid LUN creation using proportional disk mapping
US6681389B1 (en) 2000-02-28 2004-01-20 Lucent Technologies Inc. Method for providing scaleable restart and backout of software upgrades for clustered computing
WO2001082678A2 (en) 2000-05-02 2001-11-08 Sun Microsystems, Inc. Cluster membership monitor
US7219260B1 (en) 2000-05-26 2007-05-15 Emc Corporation Fault tolerant system shared system resource with state machine logging
US6990606B2 (en) 2000-07-28 2006-01-24 International Business Machines Corporation Cascading failover of a data management application for shared disk file systems in loosely coupled node clusters
US6640312B1 (en) 2000-08-01 2003-10-28 National Instruments Corporation System and method for handling device retry requests on a communication medium
US6928521B1 (en) 2000-08-01 2005-08-09 International Business Machines Corporation Method, system, and data structures for using metadata in updating data in a storage device
US6886020B1 (en) 2000-08-17 2005-04-26 Emc Corporation Method and apparatus for storage system metrics management and archive
US6567817B1 (en) 2000-09-08 2003-05-20 Hewlett-Packard Development Company, L.P. Cache management system using hashing
JP2005530242A (en) 2000-09-11 2005-10-06 アガミ システムズ, インコーポレイテッド Storage system having partitioned movable metadata
GB0025226D0 (en) 2000-10-14 2000-11-29 Ibm Data storage system and method of storing data
KR100365725B1 (en) 2000-12-27 2002-12-26 한국전자통신연구원 Ranked Cleaning Policy and Error Recovery Method for File Systems Using Flash Memory
US6950901B2 (en) 2001-01-05 2005-09-27 International Business Machines Corporation Method and apparatus for supporting parity protection in a RAID clustered environment
US6799258B1 (en) * 2001-01-10 2004-09-28 Datacore Software Corporation Methods and apparatus for point-in-time volumes
US6862692B2 (en) 2001-01-29 2005-03-01 Adaptec, Inc. Dynamic redistribution of parity groups
US6990667B2 (en) 2001-01-29 2006-01-24 Adaptec, Inc. Server-independent object positioning for load balancing drives and servers
WO2002065249A2 (en) 2001-02-13 2002-08-22 Candera, Inc. Storage virtualization and storage management to provide higher level storage services
US7542419B2 (en) 2001-04-02 2009-06-02 International Business Machines Corporation Method and apparatus for managing aggregate bandwidth at a server
US7003565B2 (en) 2001-04-03 2006-02-21 International Business Machines Corporation Clickstream data collection technique
US20020158898A1 (en) 2001-04-30 2002-10-31 Hsieh Vivian G. Graphical user interfaces for viewing and configuring devices in an automated provisioning environment
US7167965B2 (en) 2001-04-30 2007-01-23 Hewlett-Packard Development Company, L.P. Method and system for online data migration on storage systems with performance guarantees
US8171414B2 (en) 2001-05-22 2012-05-01 Netapp, Inc. System and method for consolidated reporting of characteristics for a group of file systems
US7739614B1 (en) 2001-05-22 2010-06-15 Netapp, Inc. System and method for consolidated reporting of characteristics for a group of directories
US6961865B1 (en) 2001-05-24 2005-11-01 Oracle International Corporation Techniques for resuming a transaction after an error
US7617292B2 (en) 2001-06-05 2009-11-10 Silicon Graphics International Multi-class heterogeneous clients in a clustered filesystem
US7133876B2 (en) 2001-06-12 2006-11-07 The University Of Maryland College Park Dwarf cube architecture for reducing storage sizes of multidimensional data
US20030005147A1 (en) 2001-06-29 2003-01-02 Enns Daniel Albert IP/HDLC addressing system for replacing frame relay based systems and method therefor
US7249150B1 (en) 2001-07-03 2007-07-24 Network Appliance, Inc. System and method for parallelized replay of an NVRAM log in a storage appliance
US7805266B1 (en) 2001-07-17 2010-09-28 At&T Corp. Method for automated detection of data glitches in large data sets
US6912645B2 (en) 2001-07-19 2005-06-28 Lucent Technologies Inc. Method and apparatus for archival data storage
US7174379B2 (en) 2001-08-03 2007-02-06 International Business Machines Corporation Managing server resources for hosted applications
US7366134B2 (en) 2001-08-17 2008-04-29 Comsat Corporation Dynamic allocation of network resources in a multiple-user communication system
US20040054656A1 (en) 2001-08-31 2004-03-18 Arkivio, Inc. Techniques for balancing capacity utilization in a storage environment
US7113980B2 (en) 2001-09-06 2006-09-26 Bea Systems, Inc. Exactly once JMS communication
US7028225B2 (en) 2001-09-25 2006-04-11 Path Communications, Inc. Application manager for monitoring and recovery of software based application processes
US6854035B2 (en) 2001-10-05 2005-02-08 International Business Machines Corporation Storage area network methods and apparatus for display and management of a hierarchical file system extension policy
US7096320B2 (en) 2001-10-31 2006-08-22 Hewlett-Packard Development Company, Lp. Computer performance improvement by adjusting a time used for preemptive eviction of cache entries
US6895500B1 (en) 2001-10-31 2005-05-17 Western Digital Technologies, Inc. Disk drive for receiving setup data in a self monitoring analysis and reporting technology (SMART) command
US7437472B2 (en) 2001-11-28 2008-10-14 Interactive Content Engines, Llc. Interactive broadband server system
US7693917B2 (en) 2001-11-30 2010-04-06 Intelligent Medical Objects, Inc. Method for adaptive data management
US7730153B1 (en) 2001-12-04 2010-06-01 Netapp, Inc. Efficient use of NVRAM during takeover in a node cluster
US6785771B2 (en) 2001-12-04 2004-08-31 International Business Machines Corporation Method, system, and program for destaging data in cache
US20030115204A1 (en) 2001-12-14 2003-06-19 Arkivio, Inc. Structure of policy information for storage, network and data management applications
US20030135729A1 (en) 2001-12-14 2003-07-17 I/O Integrity, Inc. Apparatus and meta data caching method for optimizing server startup performance
US7047358B2 (en) 2001-12-26 2006-05-16 Boon Storage Technologies, Inc. High-performance log-structured RAID
US7055058B2 (en) 2001-12-26 2006-05-30 Boon Storage Technologies, Inc. Self-healing log-structured RAID
US20030120869A1 (en) 2001-12-26 2003-06-26 Lee Edward K. Write-back disk cache management
US7177868B2 (en) 2002-01-02 2007-02-13 International Business Machines Corporation Method, system and program for direct client file access in a data management system
US20030135609A1 (en) * 2002-01-16 2003-07-17 Sun Microsystems, Inc. Method, system, and program for determining a modification of a system resource configuration
US7096328B2 (en) 2002-01-25 2006-08-22 University Of Southern California Pseudorandom data storage
US6748504B2 (en) 2002-02-15 2004-06-08 International Business Machines Corporation Deferred copy-on-write of a snapshot
US7177853B1 (en) 2002-02-21 2007-02-13 Emc Corporation Cache management via statistically adjusted time stamp queue
TW564349B (en) 2002-02-27 2003-12-01 Acer Labs Inc Method and related apparatus for controlling transmission interface of external device of computer system
US6801905B2 (en) 2002-03-06 2004-10-05 Sybase, Inc. Database system providing methodology for property enforcement
US7010553B2 (en) 2002-03-19 2006-03-07 Network Appliance, Inc. System and method for redirecting access to a remote mirrored snapshot
US6993539B2 (en) 2002-03-19 2006-01-31 Network Appliance, Inc. System and method for determining changes in two snapshots and for transmitting changes to destination snapshot
US7043485B2 (en) 2002-03-19 2006-05-09 Network Appliance, Inc. System and method for storage of snapshot metadata in a remote file
US7093086B1 (en) 2002-03-28 2006-08-15 Veritas Operating Corporation Disaster recovery and backup using virtual machines
US7191357B2 (en) 2002-03-29 2007-03-13 Panasas, Inc. Hybrid quorum/primary-backup fault-tolerance model
US6820180B2 (en) 2002-04-04 2004-11-16 International Business Machines Corporation Apparatus and method of cascading backup logical volume mirrors
US7464125B1 (en) 2002-04-15 2008-12-09 Ibrix Inc. Checking the validity of blocks and backup duplicates of blocks during block reads
US7149846B2 (en) 2002-04-17 2006-12-12 Lsi Logic Corporation RAID protected external secondary memory
US6912635B2 (en) 2002-05-08 2005-06-28 Hewlett-Packard Development Company, L.P. Distributing workload evenly across storage media in a storage array
US7330430B2 (en) 2002-06-04 2008-02-12 Lucent Technologies Inc. Packet-based traffic shaping
US7096277B2 (en) 2002-08-07 2006-08-22 Intel Corporation Distributed lookup based on packet contents
US20040133590A1 (en) 2002-08-08 2004-07-08 Henderson Alex E. Tree data structure with range-specifying keys and associated methods and apparatuses
US7107385B2 (en) 2002-08-09 2006-09-12 Network Appliance, Inc. Storage virtualization by layering virtual disk objects on a file system
US7343524B2 (en) 2002-09-16 2008-03-11 Finisar Corporation Network analysis omniscent loop state machine
WO2004025476A1 (en) 2002-09-16 2004-03-25 Tigi Corporation Storage system architectures and multiple caching arrangements
US7668885B2 (en) 2002-09-25 2010-02-23 MindAgent, LLC System for timely delivery of personalized aggregations of, including currently-generated, knowledge
US8489742B2 (en) 2002-10-10 2013-07-16 Convergys Information Management Group, Inc. System and method for work management
US7152142B1 (en) 2002-10-25 2006-12-19 Copan Systems, Inc. Method for a workload-adaptive high performance storage system with data protection
US7457864B2 (en) 2002-11-27 2008-11-25 International Business Machines Corporation System and method for managing the performance of a computer system based on operational characteristics of the system components
US7028218B2 (en) 2002-12-02 2006-04-11 Emc Corporation Redundant multi-processor and logical processor configuration for a file server
US6928526B1 (en) 2002-12-20 2005-08-09 Datadomain, Inc. Efficient data storage system
US7065619B1 (en) 2002-12-20 2006-06-20 Data Domain, Inc. Efficient data storage system
US7110913B2 (en) 2002-12-23 2006-09-19 United Services Automobile Association (Usaa) Apparatus and method for managing the performance of an electronic device
US7263582B2 (en) 2003-01-07 2007-08-28 Dell Products L.P. System and method for raid configuration
US8499086B2 (en) 2003-01-21 2013-07-30 Dell Products L.P. Client load distribution
US7254648B2 (en) 2003-01-30 2007-08-07 Utstarcom, Inc. Universal broadband server system and method
WO2004072816A2 (en) 2003-02-07 2004-08-26 Lammina Systems Corporation Method and apparatus for online transaction processing
EP1595197A2 (en) 2003-02-21 2005-11-16 Caringo, Inc. Additional hash functions in content-based addressing
US7831736B1 (en) 2003-02-27 2010-11-09 Cisco Technology, Inc. System and method for supporting VLANs in an iSCSI
US7155460B2 (en) 2003-03-18 2006-12-26 Network Appliance, Inc. Write-once-read-many storage system and method for implementing the same
US6904470B1 (en) 2003-03-26 2005-06-07 Emc Corporation Device selection by a disk adapter scheduler
US7624112B2 (en) 2003-04-03 2009-11-24 Oracle International Corporation Asynchronously storing transaction information from memory to a persistent storage
EP1467294A3 (en) 2003-04-04 2005-06-01 Interuniversitair Microelektronica Centrum Vzw Design method for electronic systems using library of hardware components with performance parameters and cost functions
US7394944B2 (en) 2003-04-11 2008-07-01 Seiko Epson Corporation Method and system for finding spatial medians in a sliding window environment
US7325059B2 (en) 2003-05-15 2008-01-29 Cisco Technology, Inc. Bounded index extensible hash-based IPv6 address lookup method
US7519725B2 (en) 2003-05-23 2009-04-14 International Business Machines Corporation System and method for utilizing informed throttling to guarantee quality of service to I/O streams
US7567991B2 (en) 2003-06-25 2009-07-28 Emc Corporation Replication of snapshot using a file system copy differential
US7698115B2 (en) 2003-06-30 2010-04-13 Microsoft Corporation System and method for dynamically allocating resources in a client/server environment
US7451168B1 (en) 2003-06-30 2008-11-11 Data Domain, Inc. Incremental garbage collection of data in a secondary storage
US7181296B2 (en) 2003-08-06 2007-02-20 Asml Netherlands B.V. Method of adaptive interactive learning control and a lithographic manufacturing process and apparatus employing such a method
US8239552B2 (en) 2003-08-21 2012-08-07 Microsoft Corporation Providing client access to devices over a network
US8321955B2 (en) 2003-08-26 2012-11-27 Wu-Chang Feng Systems and methods for protecting against denial of service attacks
US8285881B2 (en) 2003-09-10 2012-10-09 Broadcom Corporation System and method for load balancing and fail over
US20050076113A1 (en) 2003-09-12 2005-04-07 Finisar Corporation Network analysis sample management process
US7487235B2 (en) 2003-09-24 2009-02-03 Dell Products L.P. Dynamically varying a raid cache policy in order to optimize throughput
US6917898B1 (en) 2003-09-30 2005-07-12 Nortel Networks Limited Generation and processing of statistical information
US7315866B2 (en) 2003-10-02 2008-01-01 Agency For Science, Technology And Research Method for incremental authentication of documents
US7451167B2 (en) 2003-10-24 2008-11-11 Network Appliance, Inc. Verification of file system log data using per-entry checksums
WO2005041474A1 (en) 2003-10-28 2005-05-06 The Foundation For The Promotion Of Industrial Science Authentication system, and remotely distributed storage system
JP2005149082A (en) 2003-11-14 2005-06-09 Hitachi Ltd Storage controller and method for controlling it
ES2383998T3 (en) 2003-11-17 2012-06-28 Telecom Italia S.P.A. Architecture of quality of service supervision, related procedure, network and computer program product
JP4516306B2 (en) 2003-11-28 2010-08-04 株式会社日立製作所 How to collect storage network performance information
WO2005057365A2 (en) 2003-12-08 2005-06-23 Ebay Inc. System to automatically regenerate software code
US8140860B2 (en) 2003-12-15 2012-03-20 International Business Machines Corporation Policy-driven file system with integrated RAID functionality
JP4244319B2 (en) 2003-12-17 2009-03-25 株式会社日立製作所 Computer system management program, recording medium, computer system management system, management device and storage device therefor
WO2005086631A2 (en) 2004-01-20 2005-09-22 Bae Systems Information And Electronic Systems Integration Inc. Multifunction receiver-on-chip for electronic warfare applications
US7701948B2 (en) 2004-01-20 2010-04-20 Nortel Networks Limited Metro ethernet service enhancements
US7321982B2 (en) 2004-01-26 2008-01-22 Network Appliance, Inc. System and method for takeover of partner resources in conjunction with coredump
US7849098B1 (en) 2004-02-06 2010-12-07 Vmware, Inc. Providing multiple concurrent access to a file system
US7373473B2 (en) 2004-03-10 2008-05-13 Leica Geosystems Hds Llc System and method for efficient storage and manipulation of extremely large amounts of scan data
US7395352B1 (en) 2004-03-12 2008-07-01 Netapp, Inc. Managing data replication relationships
US7555548B2 (en) 2004-04-07 2009-06-30 Verizon Business Global Llc Method and apparatus for efficient data collection
US7415653B1 (en) 2004-04-21 2008-08-19 Sun Microsystems, Inc. Method and apparatus for vectored block-level checksum for file system data integrity
US7334094B2 (en) 2004-04-30 2008-02-19 Network Appliance, Inc. Online clone volume splitting technique
US7251663B1 (en) 2004-04-30 2007-07-31 Network Appliance, Inc. Method and apparatus for determining if stored memory range overlaps key memory ranges where the memory address space is organized in a tree form and partition elements for storing key memory ranges
US7334095B1 (en) 2004-04-30 2008-02-19 Network Appliance, Inc. Writable clone of read-only volume
US7536424B2 (en) 2004-05-02 2009-05-19 Yoram Barzilai System and methods for efficiently managing incremental data backup revisions
US20050246362A1 (en) 2004-05-03 2005-11-03 Borland Devin P System and method for dynamci log compression in a file system
US7409582B2 (en) 2004-05-06 2008-08-05 International Business Machines Corporation Low cost raid with seamless disk failure recovery
US7814064B2 (en) 2004-05-12 2010-10-12 Oracle International Corporation Dynamic distributed consensus algorithm
US7562101B1 (en) 2004-05-28 2009-07-14 Network Appliance, Inc. Block allocation testing
US8949395B2 (en) 2004-06-01 2015-02-03 Inmage Systems, Inc. Systems and methods of event driven recovery management
WO2006012058A1 (en) 2004-06-28 2006-02-02 Japan Communications, Inc. Systems and methods for mutual authentication of network
US7366865B2 (en) 2004-09-08 2008-04-29 Intel Corporation Enqueueing entries in a packet queue referencing packets
US7490084B2 (en) 2004-09-24 2009-02-10 Oracle Corporation Deferred incorporation of updates for spatial indexes
US20060075281A1 (en) 2004-09-27 2006-04-06 Kimmel Jeffrey S Use of application-level context information to detect corrupted data in a storage system
US20060072554A1 (en) 2004-09-29 2006-04-06 Fardad Farahmand Hierarchically organizing logical trunk groups in a packet-based network
JP4560367B2 (en) 2004-10-05 2010-10-13 株式会社日立製作所 Storage network performance information collection and storage method, computer system, and program
US7257690B1 (en) 2004-10-15 2007-08-14 Veritas Operating Corporation Log-structured temporal shadow store
US8131926B2 (en) 2004-10-20 2012-03-06 Seagate Technology, Llc Generic storage container for allocating multiple data formats
US7376866B1 (en) 2004-10-22 2008-05-20 Network Appliance, Inc. Method and an apparatus to perform fast log replay
US20060101091A1 (en) 2004-10-22 2006-05-11 International Business Machines Corporation Recovering references in an extended model
US7310704B1 (en) 2004-11-02 2007-12-18 Symantec Operating Corporation System and method for performing online backup and restore of volume configuration information
US7873782B2 (en) 2004-11-05 2011-01-18 Data Robotics, Inc. Filesystem-aware block storage system, apparatus, and method
AU2005304792B2 (en) 2004-11-05 2010-07-08 Drobo, Inc. Storage system condition indicator and method
JP2006139478A (en) 2004-11-11 2006-06-01 Hitachi Ltd Disk array system
US20060112155A1 (en) 2004-11-24 2006-05-25 Agami Systems, Inc. System and method for managing quality of service for a storage system
US8984140B2 (en) 2004-12-14 2015-03-17 Hewlett-Packard Development Company, L.P. Managing connections through an aggregation of network resources providing offloaded connections between applications over a network
US7403535B2 (en) 2004-12-14 2008-07-22 Hewlett-Packard Development Company, L.P. Aggregation of network resources providing offloaded connections between applications over a network
EP1672831A1 (en) 2004-12-16 2006-06-21 Nagravision S.A. Method for transmission of digital data in a local network
US7657578B1 (en) * 2004-12-20 2010-02-02 Symantec Operating Corporation System and method for volume replication in a storage environment employing distributed block virtualization
US7386758B2 (en) 2005-01-13 2008-06-10 Hitachi, Ltd. Method and apparatus for reconstructing data in object-based storage arrays
US8180855B2 (en) 2005-01-27 2012-05-15 Netapp, Inc. Coordinated shared storage architecture
WO2006090367A2 (en) 2005-02-24 2006-08-31 Xeround Systems Ltd. Method and apparatus for distributed data management in a switching network
US7757056B1 (en) 2005-03-16 2010-07-13 Netapp, Inc. System and method for efficiently calculating storage required to split a clone volume
US7305579B2 (en) 2005-03-22 2007-12-04 Xiotech Corporation Method, apparatus and program storage device for providing intelligent rebuild order selection
US8849767B1 (en) 2005-04-13 2014-09-30 Netapp, Inc. Method and apparatus for identifying and eliminating duplicate data blocks and sharing data blocks in a storage system
WO2006109307A2 (en) 2005-04-13 2006-10-19 Discretix Technologies Ltd. Method, device, and system of selectively accessing data
US8452929B2 (en) 2005-04-21 2013-05-28 Violin Memory Inc. Method and system for storage of data in non-volatile media
US8200887B2 (en) 2007-03-29 2012-06-12 Violin Memory, Inc. Memory management system and method
US20060253749A1 (en) 2005-05-09 2006-11-09 International Business Machines Corporation Real-time memory verification in a high-availability system
US7370048B2 (en) 2005-05-27 2008-05-06 International Business Machines Corporation File storage method and apparatus
US8028329B2 (en) 2005-06-13 2011-09-27 Iamsecureonline, Inc. Proxy authentication network
US7447868B2 (en) 2005-06-15 2008-11-04 International Business Machines Corporation Using vector processors to accelerate cache lookups
US8495244B2 (en) 2005-06-29 2013-07-23 Jumpstart Wireless Corporation System and method for dynamic automatic communication path selection, distributed device synchronization and task delegation
US7720820B2 (en) 2005-07-12 2010-05-18 Microsoft Corporation Logless persistent components for enterprise applications
US8504521B2 (en) 2005-07-28 2013-08-06 Gopivotal, Inc. Distributed data management system
US7984084B2 (en) 2005-08-03 2011-07-19 SanDisk Technologies, Inc. Non-volatile memory with scheduled reclaim operations
US7451348B2 (en) 2005-08-04 2008-11-11 Dot Hill Systems Corporation Dynamic write cache size adjustment in raid controller with capacitor backup energy source
CN101356506B (en) 2005-08-25 2014-01-08 晶像股份有限公司 Smart scalable storage switch architecture
US8429630B2 (en) 2005-09-15 2013-04-23 Ca, Inc. Globally distributed utility computing cloud
US8259566B2 (en) 2005-09-20 2012-09-04 Qualcomm Incorporated Adaptive quality of service policy for dynamic networks
US7366859B2 (en) 2005-10-06 2008-04-29 Acronis Inc. Fast incremental backup method and system
US7386675B2 (en) 2005-10-21 2008-06-10 Isilon Systems, Inc. Systems and methods for using excitement values to predict future access to resources
CN100370732C (en) 2005-11-04 2008-02-20 华为技术有限公司 Charge metering method and system
US8549051B2 (en) 2005-11-04 2013-10-01 Oracle America, Inc. Unlimited file system snapshots and clones
JP4766240B2 (en) 2005-11-08 2011-09-07 日本電気株式会社 File management method, apparatus, and program
US7562140B2 (en) 2005-11-15 2009-07-14 Cisco Technology, Inc. Method and apparatus for providing trend information from network devices
US8065680B2 (en) 2005-11-15 2011-11-22 Yahoo! Inc. Data gateway for jobs management based on a persistent job table and a server table
US7640231B2 (en) 2005-11-16 2009-12-29 International Business Machines Corporation Approach based on self-evolving models for performance guarantees in a shared storage system
JP4738144B2 (en) 2005-11-28 2011-08-03 株式会社日立製作所 Information monitoring method, system and program
EP1793606A1 (en) 2005-12-05 2007-06-06 Microsoft Corporation Distribution of keys for encryption/decryption
US7752173B1 (en) 2005-12-16 2010-07-06 Network Appliance, Inc. Method and apparatus for improving data processing system performance by reducing wasted disk writes
US7546321B2 (en) 2005-12-19 2009-06-09 Yahoo! Inc. System and method for recovery from failure of a storage server in a distributed column chunk data store
US7716180B2 (en) 2005-12-29 2010-05-11 Amazon Technologies, Inc. Distributed storage system with web services client interface
US7529780B1 (en) 2005-12-30 2009-05-05 Google Inc. Conflict management during data object synchronization between client and server
US7673116B2 (en) 2006-01-17 2010-03-02 Advanced Micro Devices, Inc. Input/output memory management unit that implements memory attributes based on translation data
US20140108797A1 (en) 2006-01-26 2014-04-17 Unisys Corporation Storage communities of interest using cryptographic splitting
US7454592B1 (en) 2006-02-16 2008-11-18 Symantec Operating Corporation Block-level and hash-based single-instance storage
US7593833B2 (en) 2006-03-03 2009-09-22 At&T Intellectual Property I, L.P. System and method for determining performance of network lines
CN100423491C (en) 2006-03-08 2008-10-01 杭州华三通信技术有限公司 Virtual network storing system and network storing equipment thereof
US7739422B2 (en) 2006-03-21 2010-06-15 International Business Machines Corporation Method to improve system DMA mapping while substantially reducing memory fragmentation
US7603529B1 (en) 2006-03-22 2009-10-13 Emc Corporation Methods, systems, and computer program products for mapped logical unit (MLU) replications, storage, and retrieval in a redundant array of inexpensive disks (RAID) environment
US7647525B2 (en) 2006-03-31 2010-01-12 Emc Corporation Resumption of operations following failover in connection with triangular asynchronous replication
US7849281B2 (en) 2006-04-03 2010-12-07 Emc Corporation Method and system for implementing hierarchical permission maps in a layered volume graph
US8832045B2 (en) 2006-04-07 2014-09-09 Data Storage Group, Inc. Data compression and storage techniques
US7809685B2 (en) 2006-04-21 2010-10-05 Ricoh Co., Ltd. Secure and efficient methods for logging and synchronizing data exchanges
US8214868B2 (en) 2006-04-21 2012-07-03 Agere Systems Inc. Flexible traffic management and shaping processing for multimedia distribution
US8090908B1 (en) 2006-04-26 2012-01-03 Netapp, Inc. Single nodename cluster system for fibre channel
JP2007310772A (en) 2006-05-22 2007-11-29 Hitachi Ltd Storage system and communication control method
JP5048760B2 (en) 2006-05-24 2012-10-17 コンペレント・テクノロジーズ System and method for RAID management, reallocation, and restriping
GB0610335D0 (en) 2006-05-24 2006-07-05 Oxford Semiconductor Ltd Redundant storage of data on an array of storage devices
US9009199B2 (en) 2006-06-06 2015-04-14 Haskolinn I Reykjavik Data mining using an index tree created by recursive projection of data points on random lines
US20070300013A1 (en) 2006-06-21 2007-12-27 Manabu Kitamura Storage system having transaction monitoring capability
US8826023B1 (en) 2006-06-30 2014-09-02 Symantec Operating Corporation System and method for securing access to hash-based storage systems
US8037319B1 (en) 2006-06-30 2011-10-11 Symantec Operating Corporation System and method for securely storing cryptographic keys with encrypted data
US7885257B2 (en) 2006-07-20 2011-02-08 Oracle America, Inc. Multiple virtual network stack instances using virtual network interface cards
US7987167B1 (en) 2006-08-04 2011-07-26 Netapp, Inc. Enabling a clustered namespace with redirection
US20080065639A1 (en) 2006-08-25 2008-03-13 Netfortis, Inc. String matching engine
JP4839164B2 (en) 2006-09-15 2011-12-21 株式会社日立製作所 Performance evaluation system using hardware monitor and reconfigurable computer system
US7562203B2 (en) 2006-09-27 2009-07-14 Network Appliance, Inc. Storage defragmentation based on modified physical address and unmodified logical address
US7356442B1 (en) 2006-10-05 2008-04-08 International Business Machines Corporation End of life prediction of flash memory
US7650476B2 (en) * 2006-10-18 2010-01-19 International Business Machines Corporation System, method and computer program product for generating a consistent point in time copy of data
US8589550B1 (en) 2006-10-23 2013-11-19 Emc Corporation Asymmetric data storage system for high performance and grid computing
US8447872B2 (en) 2006-11-01 2013-05-21 Intel Corporation Load balancing in a storage system
US8719844B2 (en) 2006-11-27 2014-05-06 Morgan Stanley Merging realtime data flows
US7624231B2 (en) 2006-11-29 2009-11-24 International Business Machines Corporation Map based striping of data in a distributed volatile memory environment
US8161353B2 (en) 2007-12-06 2012-04-17 Fusion-Io, Inc. Apparatus, system, and method for validating that a correct data segment is read from a data storage device
US7620669B1 (en) 2006-12-15 2009-11-17 Netapp, Inc. System and method for enhancing log performance
US7996609B2 (en) 2006-12-20 2011-08-09 International Business Machines Corporation System and method of dynamic allocation of non-volatile memory
EP2095231B1 (en) 2006-12-22 2016-07-20 Hewlett-Packard Enterprise Development LP Computer system and method of control thereof
US7802136B2 (en) 2006-12-28 2010-09-21 Intel Corporation Compiler technique for efficient register checkpointing to support transaction roll-back
US8489811B1 (en) 2006-12-29 2013-07-16 Netapp, Inc. System and method for addressing data containers using data set identifiers
US20080168226A1 (en) 2007-01-05 2008-07-10 Accusys. Inc. Correction method for reading data of disk array system
US8655939B2 (en) 2007-01-05 2014-02-18 Digital Doors, Inc. Electromagnetic pulse (EMP) hardened information infrastructure with extractor, cloud dispersal, secure storage, content analysis and classification and method therefor
US7912437B2 (en) 2007-01-09 2011-03-22 Freescale Semiconductor, Inc. Radio frequency receiver having dynamic bandwidth control and method of operation
KR101338409B1 (en) 2007-01-25 2013-12-10 삼성전자주식회사 Method and node for generating distributed rivest shamir adleman signature in ad-hoc network
US7594138B2 (en) 2007-01-31 2009-09-22 International Business Machines Corporation System and method of error recovery for backup applications
US8380880B2 (en) 2007-02-02 2013-02-19 The Mathworks, Inc. Scalable architecture
US20080201535A1 (en) 2007-02-21 2008-08-21 Hitachi, Ltd. Method and Apparatus for Provisioning Storage Volumes
JP5219183B2 (en) 2007-03-01 2013-06-26 任天堂株式会社 Video content display program, information processing apparatus, video content display method, and video content display system
US8266116B2 (en) 2007-03-12 2012-09-11 Broadcom Corporation Method and apparatus for dual-hashing tables
US7657500B2 (en) 2007-03-12 2010-02-02 Sun Microsystems, Inc. Concurrent extensible cuckoo hashing
US20080244354A1 (en) 2007-03-28 2008-10-02 Gansha Wu Apparatus and method for redundant multi-threading with recovery
US8135900B2 (en) 2007-03-28 2012-03-13 Kabushiki Kaisha Toshiba Integrated memory management and memory management method
US9632870B2 (en) 2007-03-29 2017-04-25 Violin Memory, Inc. Memory system with multiple striping of raid groups and method for performing the same
US8533410B1 (en) 2007-03-29 2013-09-10 Netapp, Inc. Maintaining snapshot and active file system metadata in an on-disk structure of a file system
JP4448866B2 (en) 2007-03-30 2010-04-14 日立ビアメカニクス株式会社 Drawing device
US8209587B1 (en) 2007-04-12 2012-06-26 Netapp, Inc. System and method for eliminating zeroing of disk drives in RAID arrays
JP2008269462A (en) 2007-04-24 2008-11-06 Hitachi Ltd Management device and method for node
US8824686B1 (en) 2007-04-27 2014-09-02 Netapp, Inc. Cluster key synchronization
US20080270719A1 (en) 2007-04-30 2008-10-30 Cochran Robert A Method and system for efficient snapshot operations in mass-storage arrays
US7975109B2 (en) 2007-05-30 2011-07-05 Schooner Information Technology, Inc. System including a fine-grained memory and a less-fine-grained memory
JP4316636B2 (en) 2007-06-06 2009-08-19 株式会社東芝 Content distribution / browsing system, content distribution apparatus, content browsing apparatus, and program
US8082390B1 (en) 2007-06-20 2011-12-20 Emc Corporation Techniques for representing and storing RAID group consistency information
KR20090005921A (en) 2007-07-10 2009-01-14 삼성전자주식회사 Load balancing method and apparatus in symmetric multi-processor system
US8024525B2 (en) 2007-07-25 2011-09-20 Digi-Data Corporation Storage control unit with memory cache protection via recorded log
US9298417B1 (en) 2007-07-25 2016-03-29 Emc Corporation Systems and methods for facilitating management of data
US9336387B2 (en) 2007-07-30 2016-05-10 Stroz Friedberg, Inc. System, method, and computer program product for detecting access to a memory device
US7856437B2 (en) 2007-07-31 2010-12-21 Hewlett-Packard Development Company, L.P. Storing nodes representing respective chunks of files in a data store
US7949693B1 (en) 2007-08-23 2011-05-24 Osr Open Systems Resources, Inc. Log-structured host data storage
CN101803269B (en) 2007-09-18 2013-01-09 兴和株式会社 Serial data communication system and serial data communication method
US7953878B1 (en) 2007-10-09 2011-05-31 Netapp, Inc. Multi-threaded internet small computer system interface (iSCSI) socket layer
US8185614B2 (en) 2007-10-09 2012-05-22 Cleversafe, Inc. Systems, methods, and apparatus for identifying accessible dispersed digital storage vaults utilizing a centralized registry
US7809701B2 (en) 2007-10-15 2010-10-05 Telefonaktiebolaget Lm Ericsson (Publ) Method and system for performing exact match searches using multiple hash tables
US7996636B1 (en) 2007-11-06 2011-08-09 Netapp, Inc. Uniquely identifying block context signatures in a storage volume hierarchy
US8074019B2 (en) 2007-11-13 2011-12-06 Network Appliance, Inc. Preventing data loss in a storage system
US7814276B2 (en) 2007-11-20 2010-10-12 Solid State System Co., Ltd. Data cache architecture and cache algorithm used therein
US20090150537A1 (en) 2007-12-10 2009-06-11 John Fanson Data communication method for a set of hard-real time applications within a network
US9292567B2 (en) 2007-12-12 2016-03-22 Oracle International Corporation Bulk matching with update
US8583865B1 (en) 2007-12-21 2013-11-12 Emc Corporation Caching with flash-based memory
US7797279B1 (en) 2007-12-31 2010-09-14 Emc Corporation Merging of incremental data streams with prior backed-up data
US8099554B1 (en) 2007-12-31 2012-01-17 Emc Corporation System and method for flash-based data caching
US8805949B2 (en) 2008-01-16 2014-08-12 Netapp, Inc. System and method for populating a cache using behavioral adaptive policies
JP2009181206A (en) 2008-01-29 2009-08-13 Hitachi Ltd Storage system and snapshot configuration migration method
US8078918B2 (en) 2008-02-07 2011-12-13 Siliconsystems, Inc. Solid state storage subsystem that maintains and provides access to data reflective of a failure risk
US20090204636A1 (en) 2008-02-11 2009-08-13 Microsoft Corporation Multimodal object de-duplication
WO2009102425A1 (en) 2008-02-12 2009-08-20 Netapp, Inc. Hybrid media storage system architecture
US7979635B2 (en) 2008-02-14 2011-07-12 International Business Machines Corporation Apparatus and method to allocate resources in a data storage library
JP2009199199A (en) 2008-02-20 2009-09-03 Hitachi Ltd Storage system and its data write method
JP4489127B2 (en) 2008-02-29 2010-06-23 株式会社東芝 Semiconductor memory device
US8234444B2 (en) 2008-03-11 2012-07-31 International Business Machines Corporation Apparatus and method to select a deduplication protocol for a data storage library
JP5146032B2 (en) 2008-03-17 2013-02-20 富士通株式会社 I / O control method, control device, and program
US8060706B2 (en) 2008-03-28 2011-11-15 Inventec Corporation Method of allocating physical memory in specified address range under Linux system platform
US7873619B1 (en) 2008-03-31 2011-01-18 Emc Corporation Managing metadata
US8074014B2 (en) 2008-03-31 2011-12-06 Microsoft Corporation Storage systems using write off-loading
US8429096B1 (en) 2008-03-31 2013-04-23 Amazon Technologies, Inc. Resource isolation through reinforcement learning
US8855318B1 (en) 2008-04-02 2014-10-07 Cisco Technology, Inc. Master key generation and distribution for storage area network devices
US7971013B2 (en) 2008-04-30 2011-06-28 Xiotech Corporation Compensating for write speed differences between mirroring storage devices by striping
KR20090120159A (en) 2008-05-19 2009-11-24 삼성전자주식회사 Apparatus and method for combining images
US8612572B2 (en) 2008-05-30 2013-12-17 Microsoft Corporation Rule-based system for client-side quality-of-service tracking and reporting
US7979399B2 (en) 2008-06-10 2011-07-12 International Business Machines Corporation Database journaling in a multi-node environment
US8108649B2 (en) 2008-06-13 2012-01-31 International Business Machines Corporation Method of memory management for server-side scripting language runtime system
US9547589B2 (en) 2008-06-18 2017-01-17 Super Talent Technology, Corp. Endurance translation layer (ETL) and diversion of temp files for reduced flash wear of a super-endurance solid-state drive
JP2010009548A (en) 2008-06-30 2010-01-14 Toshiba Corp Storage device, control device, storage system, and storage method
US8762654B1 (en) 2008-07-02 2014-06-24 Marvell International Ltd. Selectively scheduling memory accesses in parallel based on access speeds of memory
US8214404B2 (en) 2008-07-11 2012-07-03 Avere Systems, Inc. Media aware distributed data layout
US7979671B2 (en) 2008-07-28 2011-07-12 CacheIQ, Inc. Dual hash indexing system and methodology
US8250310B2 (en) 2008-07-31 2012-08-21 International Business Machines Corporation Assigning data to NVRAM of shared access hybrid hard drives
GB0814468D0 (en) 2008-08-07 2008-09-10 Rugg Gordon Methdo of and apparatus for analysing data files
US8086799B2 (en) 2008-08-12 2011-12-27 Netapp, Inc. Scalable deduplication of stored data
US7987214B2 (en) 2008-08-29 2011-07-26 Tatu Ylonen Oy Determining the address range of a subtree of a linearized tree
CN101674233B (en) 2008-09-12 2011-09-14 中国科学院声学研究所 Peterson graph-based storage network structure and data read-write method thereof
US9032151B2 (en) 2008-09-15 2015-05-12 Microsoft Technology Licensing, Llc Method and system for ensuring reliability of cache data and metadata subsequent to a reboot
US9098519B2 (en) 2008-09-16 2015-08-04 File System Labs Llc Methods and apparatus for distributed data storage
US8127182B2 (en) 2008-09-16 2012-02-28 Lsi Corporation Storage utilization to improve reliability using impending failure triggers
EP2350875A1 (en) 2008-09-19 2011-08-03 Oracle International Corporation Storage-side storage request management
US8321834B2 (en) 2008-09-25 2012-11-27 International Business Machines Corporation Framework for automatically merging customizations to structured code that has been refactored
US8799571B1 (en) 2008-09-26 2014-08-05 Emc Corporation System and method for configuring a device array upon detecting addition of a storage device
US7873729B2 (en) 2008-09-29 2011-01-18 Verizon Patent And Licensing Inc. Server scanning system and method
US9330172B2 (en) 2008-09-29 2016-05-03 Echostar Technologies Llc Audio/video archiving system and method
US8086585B1 (en) 2008-09-30 2011-12-27 Emc Corporation Access control to block storage devices for a shared disk based file system
US8312231B1 (en) 2008-11-04 2012-11-13 Netapp, Inc. Method and system for mounting logical unit numbers (LUNS) of snapshots
CN102272731A (en) 2008-11-10 2011-12-07 弗森-艾奥公司 Apparatus, system, and method for predicting failures in solid-state storage
US9015209B2 (en) 2008-12-16 2015-04-21 Sandisk Il Ltd. Download management of discardable files
US8732139B2 (en) 2008-12-18 2014-05-20 Sap Ag Method and system for dynamically partitioning very large database indices on write-once tables
US8060470B2 (en) 2008-12-23 2011-11-15 Apple Inc. Heterogeneous database management system
JP4766498B2 (en) 2008-12-24 2011-09-07 株式会社ソニー・コンピュータエンタテインメント Method and apparatus for providing user level DMA and memory access management
US8250116B2 (en) 2008-12-31 2012-08-21 Unisys Corporation KStore data simulator directives and values processor process and files
US8495417B2 (en) 2009-01-09 2013-07-23 Netapp, Inc. System and method for redundancy-protected aggregates
TWI432959B (en) 2009-01-23 2014-04-01 Infortrend Technology Inc Storage subsystem and storage system architecture performing storage virtualization and method thereof
US8170997B2 (en) 2009-01-29 2012-05-01 Microsoft Corporation Unbundled storage transaction services
US8407436B2 (en) 2009-02-11 2013-03-26 Hitachi, Ltd. Methods and apparatus for migrating thin provisioning volumes between storage systems
JP5376983B2 (en) 2009-02-12 2013-12-25 株式会社東芝 Memory system
US8397051B2 (en) 2009-02-23 2013-03-12 Autonomy, Inc. Hybrid hash tables
US8520855B1 (en) 2009-03-05 2013-08-27 University Of Washington Encapsulation and decapsulation for data disintegration
US8145838B1 (en) 2009-03-10 2012-03-27 Netapp, Inc. Processing and distributing write logs of nodes of a cluster storage system
US8205065B2 (en) 2009-03-30 2012-06-19 Exar Corporation System and method for data deduplication
US8271615B2 (en) 2009-03-31 2012-09-18 Cloud Connex, Llc Centrally managing and monitoring software as a service (SaaS) applications
US8566507B2 (en) 2009-04-08 2013-10-22 Google Inc. Data storage device capable of recognizing and controlling multiple types of memory chips
US9940138B2 (en) 2009-04-08 2018-04-10 Intel Corporation Utilization of register checkpointing mechanism with pointer swapping to resolve multithreading mis-speculations
US8656284B2 (en) 2009-04-17 2014-02-18 Empirix Inc. Method for determining a quality of user experience while performing activities in IP networks
US8996468B1 (en) 2009-04-17 2015-03-31 Dell Software Inc. Block status mapping system for reducing virtual machine backup storage
US8560879B1 (en) 2009-04-22 2013-10-15 Netapp Inc. Data recovery for failed memory device of memory device array
US8156290B1 (en) 2009-04-23 2012-04-10 Network Appliance, Inc. Just-in-time continuous segment cleaning
US8515965B2 (en) 2010-05-18 2013-08-20 Lsi Corporation Concurrent linked-list traversal for real-time hash processing in multi-core, multi-thread network processors
US9037541B2 (en) 2009-04-30 2015-05-19 Microsoft Technology Licensing, Llc Metadata for data storage array
US8402069B2 (en) 2009-05-04 2013-03-19 Microsoft Corporation Use of delete notifications by file systems and applications to release storage space
US8166233B2 (en) 2009-07-24 2012-04-24 Lsi Corporation Garbage collection for solid state disks
US20100293147A1 (en) 2009-05-12 2010-11-18 Harvey Snow System and method for providing automated electronic information backup, storage and recovery
JP2010282281A (en) 2009-06-02 2010-12-16 Hitachi Ltd Disk array device, control method therefor, and program
US8345707B2 (en) 2009-06-03 2013-01-01 Voxer Ip Llc Method for synchronizing data maintained at a plurality of nodes
US20130298170A1 (en) 2009-06-12 2013-11-07 Cygnus Broadband, Inc. Video streaming quality of experience recovery using a video quality metric
US8478799B2 (en) 2009-06-26 2013-07-02 Simplivity Corporation Namespace file system accessing an object store
US8219562B1 (en) 2009-06-29 2012-07-10 Facebook, Inc. Efficient storage and retrieval for large number of data objects
US8615615B2 (en) 2009-07-01 2013-12-24 Lsi Corporation Load balancing with SCSI I/O referrals
US8225135B2 (en) 2009-07-07 2012-07-17 Drobo, Inc. System and method for protecting users of data storage systems against known problems
US9377960B2 (en) 2009-07-29 2016-06-28 Hgst Technologies Santa Ana, Inc. System and method of using stripes for recovering data in a flash storage system
US8255620B2 (en) 2009-08-11 2012-08-28 Texas Memory Systems, Inc. Secure Flash-based memory system with fast wipe feature
US7818525B1 (en) 2009-08-12 2010-10-19 Texas Memory Systems, Inc. Efficient reduction of read disturb errors in NAND FLASH memory
WO2011031796A2 (en) 2009-09-08 2011-03-17 Fusion-Io, Inc. Apparatus, system, and method for caching data on a solid-state storage device
US20120166749A1 (en) 2009-09-08 2012-06-28 International Business Machines Corporation Data management in solid-state storage devices and tiered storage systems
US8601222B2 (en) 2010-05-13 2013-12-03 Fusion-Io, Inc. Apparatus, system, and method for conditional and atomic storage operations
US9122579B2 (en) 2010-01-06 2015-09-01 Intelligent Intellectual Property Holdings 2 Llc Apparatus, system, and method for a storage layer
US8712972B2 (en) 2009-09-22 2014-04-29 Sybase, Inc. Query optimization with awareness of limited resource usage
US8266501B2 (en) 2009-09-29 2012-09-11 Micron Technology, Inc. Stripe based memory operation
JP5170055B2 (en) 2009-10-09 2013-03-27 富士通株式会社 Processing method, storage system, information processing apparatus, and program
US8285955B2 (en) 2009-10-16 2012-10-09 Lenovo (Singapore) Pte. Ltd. Method and apparatus for automatic solid state drive performance recovery
US8285956B2 (en) * 2009-10-22 2012-10-09 Symantec Corporation Efficient logging for asynchronously replicating volume groups
US7954021B2 (en) 2009-10-23 2011-05-31 International Business Machines Corporation Solid state drive with flash sparing
US8321648B2 (en) 2009-10-26 2012-11-27 Netapp, Inc Use of similarity hash to route data for improved deduplication in a storage server cluster
US8484439B1 (en) 2009-10-27 2013-07-09 Juniper Networks, Inc. Scalable hash tables
US20110119412A1 (en) 2009-11-13 2011-05-19 Orfitelli William A Port-splitter providing a guaranteed playout rate
US8516137B2 (en) 2009-11-16 2013-08-20 Microsoft Corporation Managing virtual hard drives as blobs
US8918897B2 (en) 2009-11-24 2014-12-23 Cleversafe, Inc. Dispersed storage network data slice integrity verification
US8417987B1 (en) 2009-12-01 2013-04-09 Netapp, Inc. Mechanism for correcting errors beyond the fault tolerant level of a raid array in a storage system
US20110153603A1 (en) 2009-12-17 2011-06-23 Yahoo! Inc. Time series storage for large-scale monitoring system
US8140821B1 (en) 2009-12-18 2012-03-20 Emc Corporation Efficient read/write algorithms and associated mapping for block-level data reduction processes
US8156306B1 (en) 2009-12-18 2012-04-10 Emc Corporation Systems and methods for using thin provisioning to reclaim space identified by data reduction processes
US8090977B2 (en) 2009-12-21 2012-01-03 Intel Corporation Performing redundant memory hopping
US8560598B2 (en) 2009-12-22 2013-10-15 At&T Intellectual Property I, L.P. Integrated adaptive anycast for content distribution
US20110153972A1 (en) 2009-12-23 2011-06-23 Quantum Corporation Free space defragmention in extent based file system
US8086896B2 (en) 2009-12-28 2011-12-27 International Business Machines Corporation Dynamically tracking virtual logical storage units
US8468368B2 (en) 2009-12-29 2013-06-18 Cleversafe, Inc. Data encryption parameter dispersal
US8555022B1 (en) 2010-01-06 2013-10-08 Netapp, Inc. Assimilation of foreign LUNS into a network storage system
US8402237B2 (en) 2010-01-08 2013-03-19 Netapp, Inc. Presentation of a read-only clone LUN to a host device as a snapshot of a parent LUN
US9058119B1 (en) 2010-01-11 2015-06-16 Netapp, Inc. Efficient data migration
WO2011090500A1 (en) 2010-01-19 2011-07-28 Rether Networks Inc. Random write optimization techniques for flash disks
US20110191522A1 (en) 2010-02-02 2011-08-04 Condict Michael N Managing Metadata and Page Replacement in a Persistent Cache in Flash Memory
US9015129B2 (en) 2010-02-09 2015-04-21 Veeam Software Ag Cross-platform object level restoration from image level backups
US8706692B1 (en) 2010-02-12 2014-04-22 Citibank, N.A. Corporate infrastructure management system
US8244978B2 (en) 2010-02-17 2012-08-14 Advanced Micro Devices, Inc. IOMMU architected TLB support
US9311184B2 (en) 2010-02-27 2016-04-12 Cleversafe, Inc. Storing raid data as encoded data slices in a dispersed storage network
US8341457B2 (en) 2010-03-11 2012-12-25 Lsi Corporation System and method for optimizing redundancy restoration in distributed data layout environments
JP5066209B2 (en) 2010-03-18 2012-11-07 株式会社東芝 Controller, data storage device, and program
US20110238857A1 (en) 2010-03-29 2011-09-29 Amazon Technologies, Inc. Committed processing rates for shared resources
US8700949B2 (en) 2010-03-30 2014-04-15 International Business Machines Corporation Reliability scheme using hybrid SSD/HDD replication with log structured management
US8793447B2 (en) 2010-03-30 2014-07-29 Netapp, Inc. Restoration of a parent LUN through modification of a read-write clone LUN as the parent LUN
US8510265B1 (en) 2010-03-31 2013-08-13 Emc Corporation Configuration utility for a data storage system using a file mapping protocol for access to distributed file systems
US8856593B2 (en) 2010-04-12 2014-10-07 Sandisk Enterprise Ip Llc Failure recovery using consensus replication in a distributed flash memory system
US8463825B1 (en) 2010-04-27 2013-06-11 Tintri Inc. Hybrid file system for virtual machine storage
US20110283048A1 (en) 2010-05-11 2011-11-17 Seagate Technology Llc Structured mapping system for a memory device
US8224935B1 (en) 2010-05-12 2012-07-17 Symantec Corporation Systems and methods for efficiently synchronizing configuration data within distributed computing systems
US8621580B2 (en) 2010-05-19 2013-12-31 Cleversafe, Inc. Retrieving access information in a dispersed storage network
US8380949B2 (en) 2010-05-20 2013-02-19 International Business Machines Corporation Managing write operations to an extent of tracks migrated between storage devices
CN102270144B (en) 2010-06-04 2014-12-10 鸿富锦精密工业(深圳)有限公司 Embedded network equipment and method for upgrading firmware by using same
US9355109B2 (en) 2010-06-11 2016-05-31 The Research Foundation For The State University Of New York Multi-tier caching
CN103080917B (en) 2010-06-18 2014-08-20 Lsi公司 Scalable storage devices
US8621269B2 (en) 2010-06-22 2013-12-31 Cleversafe, Inc. Identifying a slice name information error in a dispersed storage network
US8327103B1 (en) 2010-06-28 2012-12-04 Emc Corporation Scheduling data relocation activities using configurable fairness criteria
US20120011176A1 (en) 2010-07-07 2012-01-12 Nexenta Systems, Inc. Location independent scalable file and block storage
US10162722B2 (en) 2010-07-15 2018-12-25 Veritas Technologies Llc Virtual machine aware replication method and system
US8543611B1 (en) 2010-07-16 2013-09-24 Brian Mirtich Managing dynamic state of a physical system
US8195619B2 (en) 2010-08-17 2012-06-05 Symantec Corporation Extent reference count update system and method
WO2012025974A1 (en) 2010-08-23 2012-03-01 富士通株式会社 Data storage device and control method for data storage device
US9411517B2 (en) 2010-08-30 2016-08-09 Vmware, Inc. System software interfaces for space-optimized block devices
US8837281B2 (en) 2010-09-10 2014-09-16 Futurewei Technologies, Inc. Use of partitions to reduce flooding and filtering database size requirements in large layer two networks
US8396828B2 (en) 2010-09-14 2013-03-12 Microsoft Corporation Providing lightweight multidimensional online data storage for web service usage reporting
US8589625B2 (en) 2010-09-15 2013-11-19 Pure Storage, Inc. Scheduling of reconstructive I/O read operations in a storage environment
US8732426B2 (en) 2010-09-15 2014-05-20 Pure Storage, Inc. Scheduling of reactive I/O operations in a storage environment
JP5388976B2 (en) 2010-09-22 2014-01-15 株式会社東芝 Semiconductor memory control device
US8463991B2 (en) 2010-09-28 2013-06-11 Pure Storage Inc. Intra-device data protection in a raid array
US8775868B2 (en) 2010-09-28 2014-07-08 Pure Storage, Inc. Adaptive RAID for an SSD environment
US9348696B2 (en) 2010-10-01 2016-05-24 Pure Storage, Inc. Distributed multi-level protection in a raid array based storage system
US10430298B2 (en) 2010-10-28 2019-10-01 Microsoft Technology Licensing, Llc Versatile in-memory database recovery using logical log records
US20120109936A1 (en) 2010-10-29 2012-05-03 Nec Laboratories America, Inc. Cost-effective data layout optimization over heterogeneous storage classes
US9104326B2 (en) 2010-11-15 2015-08-11 Emc Corporation Scalable block data storage using content addressing
US8577850B1 (en) 2010-11-15 2013-11-05 Symantec Corporation Techniques for global data deduplication
US8706701B1 (en) 2010-11-18 2014-04-22 Emc Corporation Scalable cloud file system with efficient integrity checks
US8583599B2 (en) 2010-11-29 2013-11-12 Ca, Inc. Reducing data duplication in cloud storage
US8880554B2 (en) 2010-12-03 2014-11-04 Futurewei Technologies, Inc. Method and apparatus for high performance, updatable, and deterministic hash table for network equipment
KR101638436B1 (en) 2010-12-10 2016-07-12 한국전자통신연구원 Cloud storage and management method thereof
US8271462B2 (en) 2010-12-10 2012-09-18 Inventec Corporation Method for creating a index of the data blocks
US10817502B2 (en) 2010-12-13 2020-10-27 Sandisk Technologies Llc Persistent memory management
EP2652623B1 (en) 2010-12-13 2018-08-01 SanDisk Technologies LLC Apparatus, system, and method for auto-commit memory
US10817421B2 (en) 2010-12-13 2020-10-27 Sandisk Technologies Llc Persistent data structures
US9208071B2 (en) 2010-12-13 2015-12-08 SanDisk Technologies, Inc. Apparatus, system, and method for accessing memory
US9805108B2 (en) 2010-12-23 2017-10-31 Mongodb, Inc. Large distributed database clustering systems and methods
US8595595B1 (en) 2010-12-27 2013-11-26 Netapp, Inc. Identifying lost write errors in a raid array
US8468180B1 (en) * 2010-12-31 2013-06-18 Emc Corporation Porting storage metadata
US8732134B2 (en) 2011-01-25 2014-05-20 Netapp, Inc. Collection of data associated with storage systems
US8924354B2 (en) 2011-02-01 2014-12-30 Ca, Inc. Block level data replication
US9251087B2 (en) 2011-02-11 2016-02-02 SanDisk Technologies, Inc. Apparatus, system, and method for virtual memory management
US8775699B2 (en) 2011-03-01 2014-07-08 Freescale Semiconductor, Inc. Read stacking for data processor interface
US8370310B2 (en) 2011-03-04 2013-02-05 Microsoft Corporation Managing database recovery time
WO2012124100A1 (en) 2011-03-17 2012-09-20 富士通株式会社 Information processing device, storage system and write control method
US9563555B2 (en) 2011-03-18 2017-02-07 Sandisk Technologies Llc Systems and methods for storage allocation
US9158596B2 (en) 2011-03-18 2015-10-13 Oracle International Corporation Partitioned ticket locks with semi-local spinning
US8966191B2 (en) 2011-03-18 2015-02-24 Fusion-Io, Inc. Logical interface for contextual storage
US8429282B1 (en) 2011-03-22 2013-04-23 Amazon Technologies, Inc. System and method for avoiding system overload by maintaining an ideal request rate
KR101717081B1 (en) 2011-03-23 2017-03-28 삼성전자주식회사 Storage device comprising a buffer memory by using a nonvolatile-ram and volatile-ram
US8538029B2 (en) 2011-03-24 2013-09-17 Hewlett-Packard Development Company, L.P. Encryption key fragment distribution
JP4996757B1 (en) 2011-03-29 2012-08-08 株式会社東芝 Secret sharing system, apparatus and program
US8392458B2 (en) 2011-04-22 2013-03-05 Hitachi, Ltd. Information apparatus and method of controlling the same
US9201742B2 (en) 2011-04-26 2015-12-01 Brian J. Bulkowski Method and system of self-managing nodes of a distributed database cluster with a consensus algorithm
US8539008B2 (en) 2011-04-29 2013-09-17 Netapp, Inc. Extent-based storage architecture
US8812450B1 (en) 2011-04-29 2014-08-19 Netapp, Inc. Systems and methods for instantaneous cloning
US8745338B1 (en) 2011-05-02 2014-06-03 Netapp, Inc. Overwriting part of compressed data without decompressing on-disk compressed data
US8996790B1 (en) 2011-05-12 2015-03-31 Densbits Technologies Ltd. System and method for flash memory management
US8904137B1 (en) 2011-05-12 2014-12-02 Symantec Corporation Deduplication system space recycling through inode manipulation
US8346810B2 (en) 2011-05-13 2013-01-01 Simplivity Corporation Reference count propagation
US8850216B1 (en) 2011-05-19 2014-09-30 Telefonaktiebolaget Lm Ericsson (Publ) Client device and media client authentication mechanism
US8806122B2 (en) 2011-05-23 2014-08-12 International Business Machines Corporation Caching data in a storage system having multiple caches including non-volatile storage cache in a sequential access storage device
US8949568B2 (en) 2011-05-24 2015-02-03 Agency For Science, Technology And Research Memory storage device, and a related zone-based block management and mapping method
US9104460B2 (en) 2011-05-31 2015-08-11 Red Hat, Inc. Inter-cloud live migration of virtualization systems
US8990536B2 (en) 2011-06-01 2015-03-24 Schneider Electric It Corporation Systems and methods for journaling and executing device control instructions
US8650377B2 (en) 2011-06-02 2014-02-11 Hitachi, Ltd. Storage managing system, computer system, and storage managing method
US8782439B2 (en) 2011-06-06 2014-07-15 Cleversafe, Inc. Securing a data segment for storage
US8838895B2 (en) 2011-06-09 2014-09-16 21Vianet Group, Inc. Solid-state disk caching the top-K hard-disk blocks selected as a function of access frequency and a logarithmic system time
US8332357B1 (en) * 2011-06-10 2012-12-11 Microsoft Corporation Identification of moved or renamed files in file synchronization
US20120317084A1 (en) 2011-06-13 2012-12-13 Beijing Z&W Technology Consulting Co., Ltd. Method and system for achieving data de-duplication on a block-level storage virtualization device
US9639591B2 (en) 2011-06-13 2017-05-02 EMC IP Holding Company LLC Low latency replication techniques with content addressable storage
US9292530B2 (en) 2011-06-14 2016-03-22 Netapp, Inc. Object-level identification of duplicate data in a storage system
US8600949B2 (en) 2011-06-21 2013-12-03 Netapp, Inc. Deduplication in an extent-based architecture
US8261085B1 (en) 2011-06-22 2012-09-04 Media Patents, S.L. Methods, apparatus and systems to improve security in computer systems
US8572091B1 (en) 2011-06-27 2013-10-29 Amazon Technologies, Inc. System and method for partitioning and indexing table data using a composite primary key
US20120331471A1 (en) 2011-06-27 2012-12-27 Microsoft Corporation Executing molecular transactions
US20130007370A1 (en) 2011-07-01 2013-01-03 Oracle International Corporation Method and apparatus for minimizing working memory contentions in computing systems
US8917872B2 (en) 2011-07-06 2014-12-23 Hewlett-Packard Development Company, L.P. Encryption key storage with key fragment stores
US9363339B2 (en) 2011-07-12 2016-06-07 Hughes Network Systems, Llc Staged data compression, including block level long range compression, for data streams in a communications system
US20130018722A1 (en) 2011-07-13 2013-01-17 Bradd Elden Libby System and method for generating a keyword bid
US20130019057A1 (en) 2011-07-15 2013-01-17 Violin Memory, Inc. Flash disk array and controller
US8671249B2 (en) 2011-07-22 2014-03-11 Fusion-Io, Inc. Apparatus, system, and method for managing storage capacity recovery
US8930307B2 (en) 2011-09-30 2015-01-06 Pure Storage, Inc. Method for removing duplicate data from a storage array
US8788788B2 (en) 2011-08-11 2014-07-22 Pure Storage, Inc. Logical sector mapping in a flash storage array
US8806160B2 (en) 2011-08-16 2014-08-12 Pure Storage, Inc. Mapping in a storage system
US8527544B1 (en) 2011-08-11 2013-09-03 Pure Storage Inc. Garbage collection in a storage system
US8832035B2 (en) 2011-08-30 2014-09-09 Netapp, Inc. System and method for retaining deduplication in a storage object after a clone split operation
US10031646B2 (en) 2011-09-07 2018-07-24 Mcafee, Llc Computer system security dashboard
KR20130027253A (en) 2011-09-07 2013-03-15 삼성전자주식회사 Method for compressing data
US8549154B2 (en) 2011-09-09 2013-10-01 Oracle International Corporation Recovering stateful read-only database sessions
KR101289931B1 (en) 2011-09-23 2013-07-25 한양대학교 산학협력단 Method and apparatus for storing data in flash memory using address mapping with various block sizes
US20130080679A1 (en) 2011-09-26 2013-03-28 Lsi Corporation System and method for optimizing thermal management for a storage controller cache
JP2013073403A (en) 2011-09-27 2013-04-22 Fujitsu Ltd Information processor, information processing method and information processing program
US8849782B2 (en) 2011-09-30 2014-09-30 Oracle International Corporation Storage tape analytics user interface
US8943032B1 (en) 2011-09-30 2015-01-27 Emc Corporation System and method for data migration using hybrid modes
US8751657B2 (en) 2011-10-04 2014-06-10 Hitachi, Ltd. Multi-client storage system and storage system management method
JP5853569B2 (en) 2011-10-12 2016-02-09 富士通株式会社 Control method and program, and computer
US8839113B2 (en) 2011-10-26 2014-09-16 Brocade Communications Systems, Inc. Method for bridging multiple network views
US8949197B2 (en) 2011-10-31 2015-02-03 Oracle International Corporation Virtual full backups
US9009449B2 (en) 2011-11-10 2015-04-14 Oracle International Corporation Reducing power consumption and resource utilization during miss lookahead
US8990495B2 (en) 2011-11-15 2015-03-24 Emc Corporation Method and system for storing data in raid memory devices
CN102364474B (en) 2011-11-17 2014-08-20 中国科学院计算技术研究所 Metadata storage system for cluster file system and metadata management method
US9203625B2 (en) 2011-11-28 2015-12-01 Cleversafe, Inc. Transferring encoded data slices in a distributed storage network
US8824296B2 (en) 2011-12-13 2014-09-02 Telefonaktiebolaget L M Ericsson (Publ) Handling congestion in a queue without discarding new messages received from a message sender
US9461896B2 (en) 2011-12-15 2016-10-04 Riverbed Technology, Inc. Methods and systems for efficient updating of time-aligned graphs in a monitoring system
US9274838B2 (en) 2011-12-22 2016-03-01 Netapp, Inc. Dynamic instantiation and management of virtual caching appliances
US8712963B1 (en) 2011-12-22 2014-04-29 Emc Corporation Method and apparatus for content-aware resizing of data chunks for replication
US9838269B2 (en) 2011-12-27 2017-12-05 Netapp, Inc. Proportional quality of service based on client usage and system metrics
WO2013101947A1 (en) 2011-12-27 2013-07-04 Solidfire, Inc. Proportional quality of service based on client usage and system metrics
US9003021B2 (en) 2011-12-27 2015-04-07 Solidfire, Inc. Management of storage system access based on client performance and cluser health
US20130227145A1 (en) 2011-12-27 2013-08-29 Solidfire, Inc. Slice server rebalancing
US8843711B1 (en) 2011-12-28 2014-09-23 Netapp, Inc. Partial write without read-modify
KR20130076430A (en) 2011-12-28 2013-07-08 삼성전자주식회사 Adaptive copy-back method and storage device using method thereof
US8799705B2 (en) 2012-01-04 2014-08-05 Emc Corporation Data protection in a random access disk array
US9088516B2 (en) 2012-01-18 2015-07-21 F5 Networks, Inc. Virtual network services
US20150019792A1 (en) 2012-01-23 2015-01-15 The Regents Of The University Of California System and method for implementing transactions using storage device support for atomic updates and flexible interface for managing data logging
US9542227B2 (en) 2012-01-30 2017-01-10 Nvidia Corporation Parallel dynamic memory allocation using a lock-free FIFO
US9053140B2 (en) 2012-02-03 2015-06-09 Apple Inc. Enhanced B-trees with record merging
US9201804B1 (en) 2012-02-06 2015-12-01 Google Inc. Dynamically adapting the configuration of a multi-queue cache based on access patterns
KR101445025B1 (en) 2012-02-09 2014-09-26 서울시립대학교 산학협력단 Efficient raid scheme for reliable ssd
US8977893B2 (en) 2012-02-17 2015-03-10 Lsi Corporation Accelerated rebuild and zero time rebuild in raid systems
US8972568B2 (en) 2012-02-21 2015-03-03 Telefonaktiebolaget L M Ericsson (Publ) Quantifying user quality of experience by passive monitoring
EP2738679A1 (en) 2012-02-24 2014-06-04 Hitachi, Ltd. Computer program and management computer
US9165005B2 (en) 2012-02-24 2015-10-20 Simplivity Corporation Method and apparatus utilizing non-uniform hash functions for placing records in non-uniform access memory
US9203713B2 (en) 2012-03-02 2015-12-01 Payoda Inc. System and method for creating and establishing a workflow based template for a domain server or IP server in datacenters
US9417811B2 (en) 2012-03-07 2016-08-16 International Business Machines Corporation Efficient inline data de-duplication on a storage system
US20130238832A1 (en) 2012-03-07 2013-09-12 Netapp, Inc. Deduplicating hybrid storage aggregate
US8732403B1 (en) 2012-03-14 2014-05-20 Netapp, Inc. Deduplication of data blocks on storage devices
US8706971B1 (en) 2012-03-14 2014-04-22 Netapp, Inc. Caching and deduplication of data blocks in cache memory
US9460009B1 (en) 2012-03-26 2016-10-04 Emc Corporation Logical unit creation in data storage system
US20130262412A1 (en) 2012-03-28 2013-10-03 Brett Derek Hawton Method and System For Database Transaction Log Compression On SQL Server
US8943282B1 (en) 2012-03-29 2015-01-27 Emc Corporation Managing snapshots in cache-based storage systems
JP5958020B2 (en) 2012-03-30 2016-07-27 富士通株式会社 Storage system
US8812456B2 (en) 2012-03-30 2014-08-19 Netapp Inc. Systems, methods, and computer program products for scheduling processing to achieve space savings
US8918581B2 (en) 2012-04-02 2014-12-23 Microsoft Corporation Enhancing the lifetime and performance of flash-based storage
US8688652B2 (en) 2012-04-05 2014-04-01 International Business Machines Corporation Increased in-line deduplication efficiency
US9075710B2 (en) 2012-04-17 2015-07-07 SanDisk Technologies, Inc. Non-volatile key-value store
US9116625B2 (en) 2012-05-11 2015-08-25 Micron Technology, Inc. Write command overlap detection
US9141290B2 (en) 2012-05-13 2015-09-22 Emc Corporation Snapshot mechanism
US20130325828A1 (en) 2012-05-14 2013-12-05 Confio Corporation System and Method For Providing High-Availability and High-Performance Options For Transaction Log
WO2013171794A1 (en) 2012-05-17 2013-11-21 Hitachi, Ltd. Method of data migration and information storage system
US10831728B2 (en) 2012-05-29 2020-11-10 International Business Machines Corporation Application-controlled sub-LUN level data migration
US8868868B1 (en) 2012-05-31 2014-10-21 Netapp, Inc. Method and system for responding to client requests for information maintained by storage systems
EP2859437A4 (en) 2012-06-08 2016-06-08 Hewlett Packard Development Co Checkpointing using fpga
US9262320B2 (en) 2012-06-15 2016-02-16 International Business Machines Corporation Tracking transactional execution footprint
US9003162B2 (en) 2012-06-20 2015-04-07 Microsoft Technology Licensing, Llc Structuring storage based on latch-free B-trees
US20130346700A1 (en) 2012-06-21 2013-12-26 Alexander I. Tomlinson Systems and methods for managing memory
US9690703B1 (en) 2012-06-27 2017-06-27 Netapp, Inc. Systems and methods providing storage system write elasticity buffers
US8799601B1 (en) 2012-06-28 2014-08-05 Emc Corporation Techniques for managing deduplication based on recently written extents
US8904224B2 (en) 2012-07-20 2014-12-02 International Business Machines Corporation Providing replication and fail-over as a network service in data centers
US8904231B2 (en) 2012-08-08 2014-12-02 Netapp, Inc. Synchronous local and cross-site failover in clustered storage systems
US8903876B2 (en) 2012-08-15 2014-12-02 Facebook, Inc. File storage system based on coordinated exhaustible and non-exhaustible storage
US9218255B2 (en) 2012-08-27 2015-12-22 International Business Machines Corporation Multi-volume instant virtual copy freeze
US9501483B2 (en) 2012-09-18 2016-11-22 Mapr Technologies, Inc. Table format for map reduce system
US9009402B2 (en) 2012-09-20 2015-04-14 Emc Corporation Content addressable storage in legacy systems
US9318154B2 (en) 2012-09-20 2016-04-19 Dell Products L.P. Method and system for preventing unreliable data operations at cold temperatures
US8922928B2 (en) 2012-09-20 2014-12-30 Dell Products L.P. Method and system for preventing unreliable data operations at cold temperatures
US9021303B1 (en) 2012-09-24 2015-04-28 Emc Corporation Multi-threaded in-memory processing of a transaction log for concurrent access to data during log replay
US10318495B2 (en) 2012-09-24 2019-06-11 Sandisk Technologies Llc Snapshots for a non-volatile device
US9413680B1 (en) 2012-09-26 2016-08-09 Amazon Technologies, Inc. Multi-tenant throttling approaches
US8745415B2 (en) 2012-09-26 2014-06-03 Pure Storage, Inc. Multi-drive cooperation to generate an encryption key
US9146684B2 (en) 2012-09-28 2015-09-29 Netapp, Inc. Storage architecture for server flash and storage array operation
KR102002830B1 (en) 2012-09-28 2019-07-23 삼성전자 주식회사 Segment cleaning apparatus and method thereof
KR102007650B1 (en) 2012-10-05 2019-10-23 삼성전자 주식회사 Segment group considering segment cleaning apparatus and method thereof
US20140101298A1 (en) 2012-10-05 2014-04-10 Microsoft Corporation Service level agreements for a configurable distributed storage system
US9141480B2 (en) 2012-10-17 2015-09-22 Datadirect Networks, Inc. Handling failed transaction peers in a distributed hash table
KR102010624B1 (en) 2012-11-02 2019-08-13 실버레이크 모빌리티 에코시스템 에스디엔 비에이치디 Method of processing requests for digital services
US8874908B2 (en) 2012-11-07 2014-10-28 Wolfgang Raudaschl Process for storing data on a central server
US8930778B2 (en) 2012-11-15 2015-01-06 Seagate Technology Llc Read disturb effect determination
US8898113B2 (en) 2012-11-21 2014-11-25 International Business Machines Corporation Managing replicated data
US9262430B2 (en) 2012-11-22 2016-02-16 Kaminario Technologies Ltd. Deduplication in a storage system
US9449040B2 (en) 2012-11-26 2016-09-20 Amazon Technologies, Inc. Block restore ordering in a streaming restore system
US9363187B2 (en) 2012-11-28 2016-06-07 Nvidia Corporation Jitter buffering system and method of jitter buffering
US9342243B2 (en) 2012-11-28 2016-05-17 Lenovo (Beijing) Co., Ltd. Method and electronic apparatus for implementing multi-operating system
US9268695B2 (en) 2012-12-12 2016-02-23 Avago Technologies General Ip (Singapore) Pte. Ltd. Methods and structure for using region locks to divert I/O requests in a storage controller having multiple processing stacks
US9251201B2 (en) 2012-12-14 2016-02-02 Microsoft Technology Licensing, Llc Compatibly extending offload token size
US9727598B2 (en) 2012-12-19 2017-08-08 Salesforce.Com, Inc. Systems, methods, and apparatuses for fixing logical or physical corruption in databases using LSM trees
US9411718B2 (en) 2012-12-21 2016-08-09 Seagate Technology Llc Method to apply fine grain wear leveling and garbage collection
US9331936B2 (en) 2012-12-30 2016-05-03 Mellanox Technologies Ltd. Switch fabric support for overlay network features
US9459856B2 (en) 2013-01-02 2016-10-04 International Business Machines Corporation Effective migration and upgrade of virtual machines in cloud environments
US9702346B2 (en) 2013-01-09 2017-07-11 Honeywell International Inc. Synchronizing data from irregularly sampled sensors
US9141554B1 (en) 2013-01-18 2015-09-22 Cisco Technology, Inc. Methods and apparatus for data processing using data compression, linked lists and de-duplication techniques
US9495288B2 (en) 2013-01-22 2016-11-15 Seagate Technology Llc Variable-size flash translation layer
US20140215147A1 (en) 2013-01-25 2014-07-31 Hewlett-Packard Development Company, L.P. Raid storage rebuild processing
US9652376B2 (en) 2013-01-28 2017-05-16 Radian Memory Systems, Inc. Cooperative flash memory control
CN103970481B (en) 2013-01-29 2017-03-01 国际商业机器公司 The method and apparatus rebuilding memory array
US20140215170A1 (en) 2013-01-31 2014-07-31 Futurewei Technologies, Inc. Block Compression in a Key/Value Store
WO2014117298A1 (en) 2013-01-31 2014-08-07 Hewlett-Packard Development Company, L.P. Event log system
US9852055B2 (en) 2013-02-25 2017-12-26 International Business Machines Corporation Multi-level memory compression
US20140250440A1 (en) 2013-03-01 2014-09-04 Adaptive Computing Enterprises, Inc. System and method for managing storage input/output for a compute environment
US20140259000A1 (en) 2013-03-05 2014-09-11 Cisco Technology, Inc. Mitigating Issues Due to Firmware Upgrades in a Converged Network Environment
US9792120B2 (en) 2013-03-05 2017-10-17 International Business Machines Corporation Anticipated prefetching for a parent core in a multi-core chip
KR102035193B1 (en) 2013-03-11 2019-10-22 삼성전자주식회사 Method for sherching for network an electronic device thereof
US8751763B1 (en) 2013-03-13 2014-06-10 Nimbus Data Systems, Inc. Low-overhead deduplication within a block-based data storage
US9953351B1 (en) 2013-03-13 2018-04-24 Amazon Technologies, Inc. Managing resource requests that exceed reserved resource capacity
US9304901B2 (en) 2013-03-14 2016-04-05 Datadirect Networks Inc. System and method for handling I/O write requests
US10706009B2 (en) 2013-03-14 2020-07-07 Oracle International Corporation Techniques to parallelize CPU and IO work of log writes
US9195939B1 (en) 2013-03-15 2015-11-24 Cavium, Inc. Scope in decision trees
US9842053B2 (en) 2013-03-15 2017-12-12 Sandisk Technologies Llc Systems and methods for persistent cache logging
US9460024B2 (en) 2013-03-15 2016-10-04 Vmware, Inc. Latency reduction for direct memory access operations involving address translation
US9971888B2 (en) 2013-03-15 2018-05-15 Id Integration, Inc. OS security filter
US9672237B2 (en) 2013-03-15 2017-06-06 Amazon Technologies, Inc. System-wide checkpoint avoidance for distributed database systems
US20150095555A1 (en) 2013-09-27 2015-04-02 Avalanche Technology, Inc. Method of thin provisioning in a solid state disk array
CN104246722B (en) 2013-03-29 2017-02-22 株式会社东芝 Storage system for eliminating data duplication on basis of hash table, storage controller, and method
US20140304548A1 (en) 2013-04-03 2014-10-09 International Business Machines Corporation Intelligent and efficient raid rebuild technique
US20140317093A1 (en) 2013-04-22 2014-10-23 Salesforce.Com, Inc. Facilitating dynamic creation of multi-column index tables and management of customer queries in an on-demand services environment
US9213633B2 (en) 2013-04-30 2015-12-15 Seagate Technology Llc Flash translation layer with lower write amplification
US9563683B2 (en) 2013-05-14 2017-02-07 Actifio, Inc. Efficient data replication
GB2528585A (en) 2013-05-17 2016-01-27 Hitachi Ltd Storage device
US20140344539A1 (en) 2013-05-20 2014-11-20 Kaminario Technologies Ltd. Managing data in a storage system
US8849764B1 (en) 2013-06-13 2014-09-30 DataGravity, Inc. System and method of data intelligent storage
US9213706B2 (en) 2013-06-13 2015-12-15 DataGravity, Inc. Live restore for a data intelligent storage system
US9438665B1 (en) 2013-06-18 2016-09-06 Amazon Technologies, Inc. Scheduling and tracking control plane operations for distributed storage systems
US9317436B2 (en) 2013-06-21 2016-04-19 Hewlett Packard Enterprise Development Lp Cache node processing
US9519591B2 (en) 2013-06-22 2016-12-13 Microsoft Technology Licensing, Llc Latch-free, log-structured storage for multiple access methods
US9026694B1 (en) 2013-06-24 2015-05-05 Emc Corporation Techniques for workload redistibution
WO2014209984A1 (en) 2013-06-25 2014-12-31 Marvell World Trade Ltd. Adaptive cache memory controller
WO2015000503A1 (en) 2013-07-02 2015-01-08 Hitachi Data Systems Engineering UK Limited Method and apparatus for migration of a virtualized file system, data storage system for migration of a virtualized file system, and file server for use in a data storage system
US9411764B2 (en) 2013-07-23 2016-08-09 Lenovo Enterprise Solutions (Singapore) Pte. Ltd. Optimized redundant high availability SAS topology
US9432270B2 (en) 2013-07-30 2016-08-30 Draios Inc. Performance and security management of applications deployed in hosted computing environments
US9921732B2 (en) 2013-07-31 2018-03-20 Splunk Inc. Radial graphs for visualizing data in real-time
US9256631B2 (en) 2013-07-31 2016-02-09 Oracle International Corporation Building a hash table using vectorized instructions
US20150039716A1 (en) 2013-08-01 2015-02-05 Coraid, Inc. Management of a Networked Storage System Through a Storage Area Network
US9244724B2 (en) 2013-08-15 2016-01-26 Globalfoundries Inc. Management of transactional memory access requests by a cache memory
US9582198B2 (en) 2013-08-26 2017-02-28 Vmware, Inc. Compressed block map of densely-populated data structures
CN105900073B (en) 2013-08-29 2020-04-10 慧与发展有限责任合伙企业 System, computer readable medium, and method for maintaining a transaction log
US9268502B2 (en) 2013-09-16 2016-02-23 Netapp, Inc. Dense tree volume metadata organization
US9430383B2 (en) 2013-09-20 2016-08-30 Oracle International Corporation Fast data initialization
EP2852097B1 (en) 2013-09-20 2016-08-10 CoScale NV Efficient data center monitoring
US9418131B1 (en) * 2013-09-24 2016-08-16 Emc Corporation Synchronization of volumes
US9385959B2 (en) 2013-09-26 2016-07-05 Acelio, Inc. System and method for improving TCP performance in virtualized environments
US9405783B2 (en) 2013-10-02 2016-08-02 Netapp, Inc. Extent hashing technique for distributed storage architecture
US9372757B2 (en) 2013-10-18 2016-06-21 Netapp, Inc. Incremental block level backup
JP6255893B2 (en) 2013-10-24 2018-01-10 富士通株式会社 Storage control device and storage control program
US10503716B2 (en) 2013-10-31 2019-12-10 Oracle International Corporation Systems and methods for generating bit matrices for hash functions using fast filtering
US9400745B2 (en) 2013-11-06 2016-07-26 International Business Machines Corporation Physical address management in solid state memory
CN105009099B (en) 2013-11-07 2018-02-06 株式会社日立制作所 Computer system and data control method
US10073630B2 (en) 2013-11-08 2018-09-11 Sandisk Technologies Llc Systems and methods for log coordination
US9152684B2 (en) 2013-11-12 2015-10-06 Netapp, Inc. Snapshots and clones of volumes in a storage system
US9201918B2 (en) 2013-11-19 2015-12-01 Netapp, Inc. Dense tree volume metadata update logging and checkpointing
US9274901B2 (en) 2013-11-20 2016-03-01 Avago Technologies General Ip (Singapore) Pte. Ltd. I/O request mirroring in a clustered storage system
US20160149763A1 (en) 2013-11-27 2016-05-26 Carl B Ingram Systems and Methods for Providing an Administrative Framework in a Cloud Architecture
US10693955B2 (en) 2013-12-14 2020-06-23 Netapp, Inc. Techniques for SAN storage cluster synchronous disaster recovery
US9965363B2 (en) 2013-12-14 2018-05-08 Netapp, Inc. Techniques for LIF placement in SAN storage cluster synchronous disaster recovery
US9367421B2 (en) 2013-12-20 2016-06-14 Netapp, Inc. Systems, methods, and computer programs products providing relevant correlation of data source performance
US9466383B2 (en) 2013-12-30 2016-10-11 Sandisk Technologies Llc Non-volatile memory and method with adaptive logical groups
US9170746B2 (en) 2014-01-07 2015-10-27 Netapp, Inc. Clustered raid assimilation management
US9448924B2 (en) 2014-01-08 2016-09-20 Netapp, Inc. Flash optimized, log-structured layer of a file system
US9727625B2 (en) 2014-01-16 2017-08-08 International Business Machines Corporation Parallel transaction messages for database replication
US9483349B2 (en) 2014-01-17 2016-11-01 Netapp, Inc. Clustered raid data organization
US9256549B2 (en) 2014-01-17 2016-02-09 Netapp, Inc. Set-associative hash table organization for efficient storage and retrieval of data in a storage system
US9268653B2 (en) 2014-01-17 2016-02-23 Netapp, Inc. Extent metadata update logging and checkpointing
WO2015107666A1 (en) 2014-01-17 2015-07-23 株式会社日立製作所 Storage apparatus and cache control method for storage apparatus
US9454434B2 (en) 2014-01-17 2016-09-27 Netapp, Inc. File system driven raid rebuild technique
US20150301964A1 (en) 2014-02-18 2015-10-22 Alistair Mark Brinicombe Methods and systems of multi-memory, control and data plane architecture
JP6233086B2 (en) 2014-02-20 2017-11-22 富士通株式会社 Storage control device, storage system, and control program
US20150244795A1 (en) 2014-02-21 2015-08-27 Solidfire, Inc. Data syncing in a distributed system
KR102033323B1 (en) 2014-03-05 2019-10-17 한국전자통신연구원 Method for storing metadata of log-structured file system for flash memory
US20150253992A1 (en) 2014-03-10 2015-09-10 Kabushiki Kaisha Toshiba Memory system and control method
WO2015135574A1 (en) 2014-03-11 2015-09-17 Hitachi Data Systems Engineering UK Limited Computer program product, method, apparatus and data storage system for controlling write operations in the data storage system
US20150261446A1 (en) 2014-03-12 2015-09-17 Futurewei Technologies, Inc. Ddr4-onfi ssd 1-to-n bus adaptation and expansion controller
US9633056B2 (en) 2014-03-17 2017-04-25 Commvault Systems, Inc. Maintaining a deduplication database
CN104934066B (en) 2014-03-19 2018-03-27 安华高科技通用Ip(新加坡)公司 Reading interference processing in nand flash memory
WO2015142339A1 (en) 2014-03-20 2015-09-24 Hewlett-Packard Development Company, L.P. Storage system transactions
US9996549B2 (en) 2014-03-21 2018-06-12 Entangled Media Corp. Method to construct a file system based on aggregated metadata from disparate sources
US9274713B2 (en) 2014-04-03 2016-03-01 Avago Technologies General Ip (Singapore) Pte. Ltd. Device driver, method and computer-readable medium for dynamically configuring a storage controller based on RAID type, data alignment with a characteristic of storage elements and queue depth in a cache
US9697228B2 (en) 2014-04-14 2017-07-04 Vembu Technologies Private Limited Secure relational file system with version control, deduplication, and error correction
US9471428B2 (en) 2014-05-06 2016-10-18 International Business Machines Corporation Using spare capacity in solid state drives
US9823842B2 (en) 2014-05-12 2017-11-21 The Research Foundation For The State University Of New York Gang migration of virtual machines using cluster-wide deduplication
US9891993B2 (en) 2014-05-23 2018-02-13 International Business Machines Corporation Managing raid parity stripe contention
US9639546B1 (en) 2014-05-23 2017-05-02 Amazon Technologies, Inc. Object-backed block-based distributed storage
US8850108B1 (en) 2014-06-04 2014-09-30 Pure Storage, Inc. Storage cluster
US9372767B2 (en) 2014-06-06 2016-06-21 Netapp, Inc. Recovery consumer framework
US9524201B2 (en) 2014-06-19 2016-12-20 Avago Technologies General Ip (Singapore) Pte. Ltd. Safe and efficient dirty data flush for dynamic logical capacity based cache in storage systems
US9514054B2 (en) 2014-07-08 2016-12-06 Netapp, Inc. Method to persistent invalidation to ensure cache durability
US9372630B2 (en) 2014-07-09 2016-06-21 International Business Machines Corporation Migration of newly allocated data to a storage tier
EP3167373A4 (en) 2014-07-10 2018-02-28 Sios Technology Corporation Interface for orchestration and analysis of a computer environment
US9298796B2 (en) 2014-08-01 2016-03-29 Veeva Systems Inc. System and method for enterprise data management
US20160048342A1 (en) 2014-08-12 2016-02-18 Facebook, Inc. Reducing read/write overhead in a storage array
JP2016057795A (en) 2014-09-09 2016-04-21 富士通株式会社 Storage control device, storage system, and storage control program
US9524103B2 (en) 2014-09-10 2016-12-20 Netapp, Inc. Technique for quantifying logical space trapped in an extent store
US20160070644A1 (en) 2014-09-10 2016-03-10 Netapp, Inc. Offset range operation striping to improve concurrency of execution and reduce contention among resources
US20160070714A1 (en) 2014-09-10 2016-03-10 Netapp, Inc. Low-overhead restartable merge operation with efficient crash recovery
US9501359B2 (en) 2014-09-10 2016-11-22 Netapp, Inc. Reconstruction of dense tree volume metadata state across crash recovery
US20160077744A1 (en) 2014-09-11 2016-03-17 Netapp, Inc. Deferred reference count update technique for low overhead volume metadata
US9772783B2 (en) 2014-09-25 2017-09-26 Dropbox, Inc. Constructing an index to facilitate accessing a closed extent in an append-only storage system
US10164841B2 (en) 2014-10-02 2018-12-25 Pure Storage, Inc. Cloud assist for storage systems
US9208463B1 (en) 2014-10-09 2015-12-08 Splunk Inc. Thresholds for key performance indicators derived from machine data
US9846642B2 (en) 2014-10-21 2017-12-19 Samsung Electronics Co., Ltd. Efficient key collision handling
US10530880B2 (en) 2014-10-31 2020-01-07 Netapp, Inc. Scalable multiple VLAN multi-tenant networking
US9817602B2 (en) 2014-11-13 2017-11-14 Violin Systems Llc Non-volatile buffering for deduplication
US9836229B2 (en) 2014-11-18 2017-12-05 Netapp, Inc. N-way merge technique for updating volume metadata in a storage I/O stack
US20160149766A1 (en) 2014-11-21 2016-05-26 Pure Storage, Inc. Cloud based management of storage systems
US9519666B2 (en) 2014-11-27 2016-12-13 E8 Storage Systems Ltd. Snapshots and thin-provisioning in distributed storage over shared storage devices
WO2016088234A1 (en) 2014-12-04 2016-06-09 株式会社 東芝 Storage device which extends useful lifetime of non-volatile semiconductor memory of different characteristics
US9817858B2 (en) 2014-12-10 2017-11-14 Sap Se Generating hash values
US9619158B2 (en) 2014-12-17 2017-04-11 International Business Machines Corporation Two-level hierarchical log structured array architecture with minimized write amplification
US9852076B1 (en) 2014-12-18 2017-12-26 Violin Systems Llc Caching of metadata for deduplicated LUNs
JP6073854B2 (en) 2014-12-26 2017-02-01 京セラドキュメントソリューションズ株式会社 Electronic equipment and firmware recovery program
US20170351543A1 (en) 2015-01-29 2017-12-07 Hewlett Packard Enterprise Development Lp Heap data structure
US10216966B2 (en) 2015-02-25 2019-02-26 Netapp, Inc. Perturb key technique
US9658785B2 (en) 2015-03-25 2017-05-23 Amazon Technologies, Inc. Dynamic configuration of data volumes
US9378043B1 (en) 2015-05-28 2016-06-28 Altera Corporation Multilayer quality of service (QOS) for network functions virtualization platforms
US9798497B1 (en) 2015-06-08 2017-10-24 Skytap Storage area network emulation
US9678681B2 (en) 2015-06-17 2017-06-13 International Business Machines Corporation Secured multi-tenancy data in cloud-based storage environments
US9652405B1 (en) 2015-06-30 2017-05-16 EMC IP Holding Company LLC Persistence of page access heuristics in a memory centric architecture
US10565230B2 (en) 2015-07-31 2020-02-18 Netapp, Inc. Technique for preserving efficiency for replication between clusters of a network
US10394660B2 (en) 2015-07-31 2019-08-27 Netapp, Inc. Snapshot restore workflow
US9740566B2 (en) 2015-07-31 2017-08-22 Netapp, Inc. Snapshot creation workflow
US10083082B2 (en) 2015-09-07 2018-09-25 International Business Machines Corporation Efficient index checkpointing in log-structured object stores
US10331625B2 (en) 2015-09-22 2019-06-25 Facebook, Inc. Managing sequential data store
US9785525B2 (en) 2015-09-24 2017-10-10 Netapp, Inc. High availability failover manager
US20170097771A1 (en) 2015-10-01 2017-04-06 Netapp, Inc. Transaction log layout for efficient reclamation and recovery
US20170109298A1 (en) 2015-10-15 2017-04-20 Kabushiki Kaisha Toshiba Storage system that includes a plurality of routing circuits and a plurality of node modules connected thereto
US10120613B2 (en) 2015-10-30 2018-11-06 Sandisk Technologies Llc System and method for rescheduling host and maintenance operations in a non-volatile memory
US9954946B2 (en) 2015-11-24 2018-04-24 Netapp, Inc. Directory level incremental replication
US9537827B1 (en) 2015-12-21 2017-01-03 Netapp, Inc. Secure mode VLANs systems and methods
US9846539B2 (en) 2016-01-22 2017-12-19 Netapp, Inc. Recovery from low space condition of an extent store
US10515192B2 (en) 2016-02-02 2019-12-24 Vmware, Inc. Consistent snapshots and clones in an asymmetric virtual distributed file system
US10191674B2 (en) 2016-04-15 2019-01-29 Netapp, Inc. Shared dense tree repair
US10642763B2 (en) 2016-09-20 2020-05-05 Netapp, Inc. Quality of service policy sets
US10382343B2 (en) 2017-04-04 2019-08-13 Netapp, Inc. Intelligent thread management across isolated network stacks

Patent Citations (34)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6779003B1 (en) * 1999-12-16 2004-08-17 Livevault Corporation Systems and methods for backing up data files
US7188149B2 (en) * 2001-03-07 2007-03-06 Hitachi, Ltd. Storage operating data control system
US7543100B2 (en) * 2001-06-18 2009-06-02 3Par, Inc. Node controller for a data storage system
US7805583B1 (en) * 2003-04-23 2010-09-28 Emc Corporation Method and apparatus for migrating data in a clustered computer system environment
US20050027817A1 (en) * 2003-07-31 2005-02-03 Microsoft Corporation Replication protocol for data stores
US7395283B1 (en) * 2003-11-10 2008-07-01 Emc Corporation Method and apparatus for making independent data copies in a data processing system
US8055745B2 (en) * 2004-06-01 2011-11-08 Inmage Systems, Inc. Methods and apparatus for accessing data from a primary data storage system for secondary storage
US20090157870A1 (en) * 2005-09-20 2009-06-18 Nec Corporation Resource-amount calculation system, and method and program thereof
US20070088702A1 (en) * 2005-10-03 2007-04-19 Fridella Stephen A Intelligent network client for multi-protocol namespace redirection
US20070083482A1 (en) * 2005-10-08 2007-04-12 Unmesh Rathi Multiple quality of service file system
US7962709B2 (en) * 2005-12-19 2011-06-14 Commvault Systems, Inc. Network redirector systems and methods for performing data replication
US20070186066A1 (en) * 2006-02-03 2007-08-09 Emc Corporation Fast verification of computer backup data
US20070186127A1 (en) * 2006-02-03 2007-08-09 Emc Corporation Verification of computer backup data
US20070208918A1 (en) * 2006-03-01 2007-09-06 Kenneth Harbin Method and apparatus for providing virtual machine backup
US7817562B1 (en) * 2006-09-29 2010-10-19 Emc Corporation Methods and systems for back end characterization using I/O sampling
US20090271412A1 (en) * 2008-04-29 2009-10-29 Maxiscale, Inc. Peer-to-Peer Redundant File Server System and Methods
US20120003940A1 (en) * 2009-03-25 2012-01-05 Nec Corporation Communication device, recording medium for control program of communication device, communication system and communication method
US8122213B2 (en) * 2009-05-05 2012-02-21 Dell Products L.P. System and method for migration of data
US8671265B2 (en) * 2010-03-05 2014-03-11 Solidfire, Inc. Distributed data storage system providing de-duplication of data using block identifiers
US8555019B2 (en) * 2010-09-08 2013-10-08 International Business Machines Corporation Using a migration cache to cache tracks during migration
US9411620B2 (en) * 2010-11-29 2016-08-09 Huawei Technologies Co., Ltd. Virtual storage migration method, virtual storage migration system and virtual machine monitor
US20120317353A1 (en) * 2011-06-13 2012-12-13 XtremlO Ltd. Replication techniques with content addressable storage
US20130007097A1 (en) * 2011-06-30 2013-01-03 Hitachi, Ltd. Server system and method for controlling information system
US20130073519A1 (en) * 2011-09-20 2013-03-21 Netapp, Inc. Handling data extent size asymmetry during logical replication in a storage system
US20140108350A1 (en) * 2011-09-23 2014-04-17 Hybrid Logic Ltd System for live-migration and automated recovery of applications in a distributed system
US20130138616A1 (en) * 2011-11-29 2013-05-30 International Business Machines Corporation Synchronizing updates across cluster filesystems
US20130232261A1 (en) * 2011-12-27 2013-09-05 Solidfire, Inc. Quality of service policy sets
US20130185719A1 (en) * 2012-01-17 2013-07-18 Microsoft Corporation Throttling guest write ios based on destination throughput
US9092142B2 (en) * 2012-06-26 2015-07-28 Hitachi, Ltd. Storage system and method of controlling the same
US20140006353A1 (en) * 2012-06-28 2014-01-02 International Business Machines Corporation Recording backup information for backed-up data items in a data item list
US9047211B2 (en) * 2013-03-15 2015-06-02 SanDisk Technologies, Inc. Managing data reliability
US20140310231A1 (en) * 2013-04-16 2014-10-16 Cognizant Technology Solutions India Pvt. Ltd. System and method for automating data warehousing processes
US20140344222A1 (en) * 2013-05-16 2014-11-20 Oracle International Corporation Method and apparatus for replication size estimation and progress monitoring
US20150066852A1 (en) * 2013-08-27 2015-03-05 Netapp, Inc. Detecting out-of-band (oob) changes when replicating a source file system using an in-line system

Cited By (155)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11379119B2 (en) 2010-03-05 2022-07-05 Netapp, Inc. Writing data in a distributed data storage system
US10951488B2 (en) 2011-12-27 2021-03-16 Netapp, Inc. Rule-based performance class access management for storage cluster performance guarantees
US11212196B2 (en) 2011-12-27 2021-12-28 Netapp, Inc. Proportional quality of service based on client impact on an overload condition
US10911328B2 (en) 2011-12-27 2021-02-02 Netapp, Inc. Quality of service policy based load adaption
US20160110261A1 (en) * 2013-05-07 2016-04-21 Axcient, Inc. Cloud storage using merkle trees
US9923762B1 (en) * 2013-08-13 2018-03-20 Ca, Inc. Upgrading an engine when a scenario is running
US11386120B2 (en) 2014-02-21 2022-07-12 Netapp, Inc. Data syncing in a distributed system
US10826795B2 (en) 2014-05-05 2020-11-03 Nutanix, Inc. Architecture for implementing service level management for a virtualization environment
US9798728B2 (en) 2014-07-24 2017-10-24 Netapp, Inc. System performing data deduplication using a dense tree data structure
US9671960B2 (en) 2014-09-12 2017-06-06 Netapp, Inc. Rate matching technique for balancing segment cleaning and I/O workload
US10210082B2 (en) 2014-09-12 2019-02-19 Netapp, Inc. Rate matching technique for balancing segment cleaning and I/O workload
US10133511B2 (en) 2014-09-12 2018-11-20 Netapp, Inc Optimized segment cleaning technique
US10176117B2 (en) * 2014-10-01 2019-01-08 Cacheio Llc Efficient metadata in a storage system
US20170300424A1 (en) * 2014-10-01 2017-10-19 Cacheio Llc Efficient metadata in a storage system
US10365838B2 (en) 2014-11-18 2019-07-30 Netapp, Inc. N-way merge technique for updating volume metadata in a storage I/O stack
US9836229B2 (en) 2014-11-18 2017-12-05 Netapp, Inc. N-way merge technique for updating volume metadata in a storage I/O stack
US9720779B2 (en) * 2014-11-27 2017-08-01 Institute For Information Industry Backup system and backup method thereof
US20160154704A1 (en) * 2014-11-27 2016-06-02 Institute For Information Industry Backup system and backup method thereof
US20170295239A1 (en) * 2014-12-27 2017-10-12 Huawei Technologies Co.,Ltd. Data processing method, apparatus, and system
US11032368B2 (en) 2014-12-27 2021-06-08 Huawei Technologies Co., Ltd. Data processing method, apparatus, and system
US11799959B2 (en) 2014-12-27 2023-10-24 Huawei Technologies Co., Ltd. Data processing method, apparatus, and system
US9720601B2 (en) 2015-02-11 2017-08-01 Netapp, Inc. Load balancing technique for a storage array
US9762460B2 (en) 2015-03-24 2017-09-12 Netapp, Inc. Providing continuous context for operational information of a storage system
US9710317B2 (en) 2015-03-30 2017-07-18 Netapp, Inc. Methods to identify, handle and recover from suspect SSDS in a clustered flash array
US9740566B2 (en) 2015-07-31 2017-08-22 Netapp, Inc. Snapshot creation workflow
US10223035B2 (en) * 2015-08-28 2019-03-05 Vmware, Inc. Scalable storage space allocation in distributed storage systems
US20170060432A1 (en) * 2015-08-28 2017-03-02 Vmware, Inc. Scalable storage space allocation in distributed storage systems
US10725976B2 (en) 2015-09-22 2020-07-28 International Business Machines Corporation Fast recovery using self-describing replica files in a distributed storage system
US10127243B2 (en) * 2015-09-22 2018-11-13 International Business Machines Corporation Fast recovery using self-describing replica files in a distributed storage system
US11106645B1 (en) * 2015-09-29 2021-08-31 EMC IP Holding Company LLC Multi point in time object store
US9613046B1 (en) * 2015-12-14 2017-04-04 Netapp, Inc. Parallel optimized remote synchronization of active block storage
US11226985B2 (en) * 2015-12-15 2022-01-18 Microsoft Technology Licensing, Llc Replication of structured data records among partitioned data storage spaces
US9740570B2 (en) * 2015-12-18 2017-08-22 Dropbox, Inc. Network folder resynchronization
US20170177445A1 (en) * 2015-12-18 2017-06-22 Dropbox, Inc. Network folder resynchronization
US11449391B2 (en) 2015-12-18 2022-09-20 Dropbox, Inc. Network folder resynchronization
US10585759B2 (en) 2015-12-18 2020-03-10 Dropbox, Inc. Network folder resynchronization
US10949309B2 (en) * 2015-12-28 2021-03-16 Netapp Inc. Snapshot creation with synchronous replication
US10929022B2 (en) 2016-04-25 2021-02-23 Netapp. Inc. Space savings reporting for storage system supporting snapshot and clones
US11327910B2 (en) 2016-09-20 2022-05-10 Netapp, Inc. Quality of service policy sets
US11886363B2 (en) 2016-09-20 2024-01-30 Netapp, Inc. Quality of service policy sets
US10997098B2 (en) 2016-09-20 2021-05-04 Netapp, Inc. Quality of service policy sets
US11023539B2 (en) 2016-09-26 2021-06-01 Splunk Inc. Data intake and query system search functionality in a data fabric service system
US11126632B2 (en) 2016-09-26 2021-09-21 Splunk Inc. Subquery generation based on search configuration data from an external data system
US11580107B2 (en) 2016-09-26 2023-02-14 Splunk Inc. Bucket data distribution for exporting data to worker nodes
US11586627B2 (en) 2016-09-26 2023-02-21 Splunk Inc. Partitioning and reducing records at ingest of a worker node
US11567993B1 (en) 2016-09-26 2023-01-31 Splunk Inc. Copying buckets from a remote shared storage system to memory associated with a search node for query execution
US11593377B2 (en) 2016-09-26 2023-02-28 Splunk Inc. Assigning processing tasks in a data intake and query system
US11562023B1 (en) 2016-09-26 2023-01-24 Splunk Inc. Merging buckets in a data intake and query system
US11550847B1 (en) 2016-09-26 2023-01-10 Splunk Inc. Hashing bucket identifiers to identify search nodes for efficient query execution
US11599541B2 (en) 2016-09-26 2023-03-07 Splunk Inc. Determining records generated by a processing task of a query
US10776355B1 (en) 2016-09-26 2020-09-15 Splunk Inc. Managing, storing, and caching query results and partial query results for combination with additional query results
US11232100B2 (en) 2016-09-26 2022-01-25 Splunk Inc. Resource allocation for multiple datasets
US11461334B2 (en) 2016-09-26 2022-10-04 Splunk Inc. Data conditioning for dataset destination
US11222066B1 (en) 2016-09-26 2022-01-11 Splunk Inc. Processing data using containerized state-free indexing nodes in a containerized scalable environment
US10956415B2 (en) 2016-09-26 2021-03-23 Splunk Inc. Generating a subquery for an external data system using a configuration file
US10977260B2 (en) * 2016-09-26 2021-04-13 Splunk Inc. Task distribution in an execution node of a distributed execution environment
US11604795B2 (en) 2016-09-26 2023-03-14 Splunk Inc. Distributing partial results from an external data system between worker nodes
US10984044B1 (en) 2016-09-26 2021-04-20 Splunk Inc. Identifying buckets for query execution using a catalog of buckets stored in a remote shared storage system
US11442935B2 (en) 2016-09-26 2022-09-13 Splunk Inc. Determining a record generation estimate of a processing task
US11615104B2 (en) 2016-09-26 2023-03-28 Splunk Inc. Subquery generation based on a data ingest estimate of an external data system
US11003714B1 (en) 2016-09-26 2021-05-11 Splunk Inc. Search node and bucket identification using a search node catalog and a data store catalog
US11392654B2 (en) 2016-09-26 2022-07-19 Splunk Inc. Data fabric service system
US11620336B1 (en) 2016-09-26 2023-04-04 Splunk Inc. Managing and storing buckets to a remote shared storage system based on a collective bucket size
US11636105B2 (en) 2016-09-26 2023-04-25 Splunk Inc. Generating a subquery for an external data system using a configuration file
US11010435B2 (en) 2016-09-26 2021-05-18 Splunk Inc. Search service for a data fabric system
US11341131B2 (en) 2016-09-26 2022-05-24 Splunk Inc. Query scheduling based on a query-resource allocation and resource availability
US11238112B2 (en) 2016-09-26 2022-02-01 Splunk Inc. Search service system monitoring
US11023463B2 (en) 2016-09-26 2021-06-01 Splunk Inc. Converting and modifying a subquery for an external data system
US11663227B2 (en) 2016-09-26 2023-05-30 Splunk Inc. Generating a subquery for a distinct data intake and query system
US11797618B2 (en) 2016-09-26 2023-10-24 Splunk Inc. Data fabric service system deployment
US11080345B2 (en) 2016-09-26 2021-08-03 Splunk Inc. Search functionality of worker nodes in a data fabric service system
US11321321B2 (en) 2016-09-26 2022-05-03 Splunk Inc. Record expansion and reduction based on a processing task in a data intake and query system
US11860940B1 (en) 2016-09-26 2024-01-02 Splunk Inc. Identifying buckets for query execution using a catalog of buckets
US11106734B1 (en) 2016-09-26 2021-08-31 Splunk Inc. Query execution using containerized state-free search nodes in a containerized scalable environment
US11314753B2 (en) 2016-09-26 2022-04-26 Splunk Inc. Execution of a query received from a data intake and query system
US11586692B2 (en) 2016-09-26 2023-02-21 Splunk Inc. Streaming data processing
US11294941B1 (en) 2016-09-26 2022-04-05 Splunk Inc. Message-based data ingestion to a data intake and query system
US11874691B1 (en) 2016-09-26 2024-01-16 Splunk Inc. Managing efficient query execution including mapping of buckets to search nodes
US11163758B2 (en) 2016-09-26 2021-11-02 Splunk Inc. External dataset capability compensation
US11281706B2 (en) 2016-09-26 2022-03-22 Splunk Inc. Multi-layer partition allocation for query execution
US11176208B2 (en) 2016-09-26 2021-11-16 Splunk Inc. Search functionality of a data intake and query system
US11269939B1 (en) 2016-09-26 2022-03-08 Splunk Inc. Iterative message-based data processing including streaming analytics
US11250056B1 (en) 2016-09-26 2022-02-15 Splunk Inc. Updating a location marker of an ingestion buffer based on storing buckets in a shared storage system
US11243963B2 (en) 2016-09-26 2022-02-08 Splunk Inc. Distributing partial results to worker nodes from an external data system
US11194763B1 (en) * 2016-09-29 2021-12-07 Triad National Security, Llc Scalable augmented enumeration and metadata operations for large filesystems
US10951465B1 (en) * 2016-09-29 2021-03-16 Emc Ïp Holding Company Llc Distributed file system analytics
US10949387B1 (en) * 2016-09-29 2021-03-16 Triad National Security, Llc Scalable filesystem enumeration and metadata operations
US10331362B1 (en) * 2016-09-30 2019-06-25 EMC IP Holding Company LLC Adaptive replication for segmentation anchoring type
US11921672B2 (en) 2017-07-31 2024-03-05 Splunk Inc. Query execution at a remote heterogeneous data store of a data fabric service
US11151137B2 (en) 2017-09-25 2021-10-19 Splunk Inc. Multi-partition operation in combination operations
US11860874B2 (en) 2017-09-25 2024-01-02 Splunk Inc. Multi-partitioning data for combination operations
US11500875B2 (en) 2017-09-25 2022-11-15 Splunk Inc. Multi-partitioning for combination operations
US20190197562A1 (en) * 2017-12-27 2019-06-27 Irene Woerner System and method for product authentication
US11704336B2 (en) 2017-12-28 2023-07-18 Dropbox, Inc. Efficient filename storage and retrieval
US11120039B2 (en) 2017-12-28 2021-09-14 Dropbox, Inc. Updating a remote tree for a client synchronization service
US10949445B2 (en) 2017-12-28 2021-03-16 Dropbox, Inc. Content management client synchronization service
US11461365B2 (en) 2017-12-28 2022-10-04 Dropbox, Inc. Atomic moves with lamport clocks in a content management system
US11080297B2 (en) 2017-12-28 2021-08-03 Dropbox, Inc. Incremental client synchronization
US11048720B2 (en) 2017-12-28 2021-06-29 Dropbox, Inc. Efficiently propagating diff values
US11836151B2 (en) 2017-12-28 2023-12-05 Dropbox, Inc. Synchronizing symbolic links
US10599673B2 (en) 2017-12-28 2020-03-24 Dropbox, Inc. Content management client synchronization service
US11016991B2 (en) 2017-12-28 2021-05-25 Dropbox, Inc. Efficient filename storage and retrieval
US10671638B2 (en) 2017-12-28 2020-06-02 Dropbox, Inc. Allocation and reassignment of unique identifiers for synchronization of content items
US11010402B2 (en) 2017-12-28 2021-05-18 Dropbox, Inc. Updating a remote tree for a client synchronization service
US11003685B2 (en) 2017-12-28 2021-05-11 Dropbox, Inc. Commit protocol for synchronizing content items
US11423048B2 (en) 2017-12-28 2022-08-23 Dropbox, Inc. Content management client synchronization service
US11429634B2 (en) 2017-12-28 2022-08-30 Dropbox, Inc. Storage interface for synchronizing content
US11782949B2 (en) 2017-12-28 2023-10-10 Dropbox, Inc. Violation resolution in client synchronization
US10762104B2 (en) 2017-12-28 2020-09-01 Dropbox, Inc. File journal interface for synchronizing content
US11188559B2 (en) 2017-12-28 2021-11-30 Dropbox, Inc. Directory snapshots with searchable file paths
US11475041B2 (en) 2017-12-28 2022-10-18 Dropbox, Inc. Resynchronizing metadata in a content management system
US11176164B2 (en) 2017-12-28 2021-11-16 Dropbox, Inc. Transition to an organization directory
US11669544B2 (en) 2017-12-28 2023-06-06 Dropbox, Inc. Allocation and reassignment of unique identifiers for synchronization of content items
US11500897B2 (en) 2017-12-28 2022-11-15 Dropbox, Inc. Allocation and reassignment of unique identifiers for synchronization of content items
US10324903B1 (en) * 2017-12-28 2019-06-18 Dropbox, Inc. Content management client synchronization service
US11500899B2 (en) 2017-12-28 2022-11-15 Dropbox, Inc. Efficient management of client synchronization updates
US11514078B2 (en) 2017-12-28 2022-11-29 Dropbox, Inc. File journal interface for synchronizing content
US10691720B2 (en) 2017-12-28 2020-06-23 Dropbox, Inc. Resynchronizing metadata in a content management system
US10936622B2 (en) 2017-12-28 2021-03-02 Dropbox, Inc. Storage interface for synchronizing content
US10929427B2 (en) 2017-12-28 2021-02-23 Dropbox, Inc. Selective synchronization of content items in a content management system
US10922333B2 (en) 2017-12-28 2021-02-16 Dropbox, Inc. Efficient management of client synchronization updates
US11657067B2 (en) 2017-12-28 2023-05-23 Dropbox Inc. Updating a remote tree for a client synchronization service
US10726044B2 (en) 2017-12-28 2020-07-28 Dropbox, Inc. Atomic moves with lamport clocks in a content management system
US10877993B2 (en) 2017-12-28 2020-12-29 Dropbox, Inc. Updating a local tree for a client synchronization service
US10872098B2 (en) 2017-12-28 2020-12-22 Dropbox, Inc. Allocation and reassignment of unique identifiers for synchronization of content items
US10866964B2 (en) 2017-12-28 2020-12-15 Dropbox, Inc. Updating a local tree for a client synchronization service
US10789269B2 (en) 2017-12-28 2020-09-29 Dropbox, Inc. Resynchronizing metadata in a content management system
US10776386B2 (en) 2017-12-28 2020-09-15 Dropbox, Inc. Content management client synchronization service
US10733205B2 (en) 2017-12-28 2020-08-04 Dropbox, Inc. Violation resolution in client synchronization
US11579978B2 (en) 2018-02-14 2023-02-14 Rubrik, Inc. Fileset partitioning for data storage and management
US11334543B1 (en) 2018-04-30 2022-05-17 Splunk Inc. Scalable bucket merging for a data intake and query system
US11720537B2 (en) 2018-04-30 2023-08-08 Splunk Inc. Bucket merging for a data intake and query system using size thresholds
US20200073962A1 (en) * 2018-08-29 2020-03-05 International Business Machines Corporation Checkpointing for increasing efficiency of a blockchain
US11334439B2 (en) 2018-08-29 2022-05-17 International Business Machines Corporation Checkpointing for increasing efficiency of a blockchain
US10901957B2 (en) * 2018-08-29 2021-01-26 International Business Machines Corporation Checkpointing for increasing efficiency of a blockchain
US11196542B2 (en) 2018-08-29 2021-12-07 International Business Machines Corporation Checkpointing for increasing efficiency of a blockchain
US11620191B2 (en) * 2018-10-01 2023-04-04 Rubrik, Inc. Fileset passthrough using data management and storage node
US11010351B1 (en) * 2018-10-31 2021-05-18 EMC IP Holding Company LLC File system replication between software defined network attached storage processes using file system snapshots
US11144401B2 (en) * 2018-11-16 2021-10-12 Vmware, Inc. Component aware incremental backup, restore, and reconciliation solution
US11615087B2 (en) 2019-04-29 2023-03-28 Splunk Inc. Search time estimate in a data intake and query system
US11715051B1 (en) 2019-04-30 2023-08-01 Splunk Inc. Service provider instance recommendations using machine-learned classifications and reconciliation
CN110730228A (en) * 2019-10-10 2020-01-24 深圳市网心科技有限公司 Data storage method, electronic device, system and medium
CN112685378A (en) * 2019-10-17 2021-04-20 伊姆西Ip控股有限责任公司 Method, apparatus and computer-readable storage medium for garbage collection
US20210117275A1 (en) * 2019-10-17 2021-04-22 EMC IP Holding Company LLC Method, device and computer readalbe medium for garbage collection
US11494380B2 (en) 2019-10-18 2022-11-08 Splunk Inc. Management of distributed computing framework components in a data fabric service system
US11216204B2 (en) * 2019-11-19 2022-01-04 Netapp, Inc. Degraded redundant metadata, DRuM, technique
US11797565B2 (en) * 2019-12-30 2023-10-24 Paypal, Inc. Data validation using encode values
US11922222B1 (en) 2020-01-30 2024-03-05 Splunk Inc. Generating a modified component for a data intake and query system using an isolated execution environment image
US11669428B2 (en) 2020-05-19 2023-06-06 Paypal, Inc. Detection of matching datasets using encode values
US20220043799A1 (en) * 2020-08-07 2022-02-10 EMC IP Holding Company LLC Method, device, and computer program product for metadata comparison
US11704313B1 (en) 2020-10-19 2023-07-18 Splunk Inc. Parallel branch operation using intermediary nodes
US11868656B2 (en) 2021-06-07 2024-01-09 Netapp, Inc. Distributed file system with disaggregated data management and storage management layers
US20220391359A1 (en) * 2021-06-07 2022-12-08 Netapp, Inc. Distributed File System that Provides Scalability and Resiliency
US11835990B2 (en) 2021-11-16 2023-12-05 Netapp, Inc. Use of cluster-level redundancy within a cluster of a distributed storage management system to address node-level errors
US11934280B2 (en) 2021-11-16 2024-03-19 Netapp, Inc. Use of cluster-level redundancy within a cluster of a distributed storage management system to address node-level errors

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US20150242478A1 (en) 2015-08-27
US11386120B2 (en) 2022-07-12

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