This disclosure relates in general to the field of data storage and, in particular, to tenant-level sharding of disks with tenant-specific storage modules to enable policies per tenant in a distributed storage system.
In recent years, cloud-based storage has emerged to offer a solution for storing, accessing, and protecting electronic data owned or controlled by all types of private and public entities. Distributed storage systems may offer a storage platform designed to provide object based, block based, and file based storage from a single distributed storage cluster in a cloud. A distributed storage cluster may contain numerous nodes for storing objects and other data. Generally, a single storage cluster of a distributed storage system, such as Ceph, is designed to accommodate data from multiple tenants, where the same set of rules and weights apply to all of the tenants. Typically, data belonging to the multiple tenants share the same storage device daemons or other software and disk partitions. Tenants, however, sometimes prefer to receive particular types and levels of service.
To provide a more complete understanding of the present disclosure and features and advantages thereof, reference is made to the following description, taken in conjunction with the accompanying figures, wherein like reference numerals represent like parts, in which:
The present disclosure describes a recovery system for a distributed storage system. A method is provided in one example of the present disclosure and includes receiving an indication of a data storage module to be associated with a tenant of a distributed storage system, allocating a partition of a disk for data of the tenant, creating a first association between the data storage module and the disk partition, creating a second association between the data storage module and the tenant, and creating rules for the data storage module based on one or more policies configured for the tenant.
In specific embodiments, the method further includes receiving an indication of a type of subscription model selected for the tenant, and selecting the disk partition to be allocated based, at least in part, on the subscription model selected for the tenant. In further specific embodiments, the data storage module can store at least some data of the tenant in the disk partition based, at least in part, on the rules. In some embodiments, the one or more policies are related to at least one of a performance requirement of disks to store the data, a distribution requirement for the data, and a replication requirement for the data.
In at least some embodiments, the method includes generating a storage map indicating the first association between the data storage module and the disk partition and indicating the second association between the data storage module and the tenant. The method may also include generating a unique identifier of the data storage module, wherein the unique identifier of the data storage module is mapped to the tenant in the storage map, and wherein the unique identifier is not mapped to any other tenants in the storage map. In specific implementations, the storage map includes a mapping of the unique identifier of the data storage module to the tenant and a mapping of the unique identifier of the data storage module to the disk partition.
In at least some embodiments, one or more other partitions of the disk are associated with one or more other data storage modules, respectively, and the one or more other data storage modules are associated with one or more other tenants, respectively, of the distributed storage system. The disk partition can include a portion of the disk or all of the disk, and only data of the tenant is to be stored in the disk partition.
In at least some embodiments, the method can further include receiving an indication of a second data storage module to be associated with the tenant of the distributed storage system, allocating a second disk partition for other data of the tenant, creating a third association between the second data storage module and the second disk partition, creating a fourth association between the second data storage module and the tenant, and creating other rules for the second data storage module based on one or more other policies configured for storing other data of the tenant. The data may include one of objects, files or blocks. In at least one embodiment, the distributed storage system is a Ceph storage system.
Some or all of the elements, operations, and features may be included in respective systems, apparatuses, and devices for performing the described functionality. Furthermore, some or all of the features may be implemented in at least one machine readable storage medium.
Elements of
For purposes of illustrating the techniques of administration host 10, it is important to understand the activities that may be present in a distributed storage system. The following foundational information may be viewed as a basis from which the present disclosure may be properly explained. Such information is offered for purposes of explanation only and, accordingly, should not be construed in any way to limit the broad scope of the present disclosure and its potential applications.
Distributed storage systems have emerged to provide a scalable option for cloud storage with greater accessibility and protection of stored data. Object storage involves storing chunks of data in an object, with each object including metadata and a unique identifier. Distributed storage systems can also be applied to other types of data storage such as block storage and file storage, for example. In block storage, data can be stored in blocks (or volumes) where each block acts as an individual hard drive. File storage is generally a hierarchical way of organizing files containing data such that an individual file can be located by a path to that file. Certain metadata describing the file and its contents is also typically stored in the file system. In distributed storage systems, multiple replicas of data in any suitable type of structure (e.g., objects, files, blocks, etc.) can be maintained in order to provide fault tolerance and high availability. Although embodiments may be described herein with reference to objects and distributed object storage, this is done for ease of illustration and it should be understood that these embodiments may also be applicable to other types of data storage structures (e.g., file, block, etc.) and distributed storage including, but not limited to distributed file storage and distributed block storage.
An example distributed storage system that provides high fault tolerance and availability includes Ceph, which is described by Sage A. Weil in the dissertation, “Ceph: Reliable, Scalable, and High-Performance Distributed Storage,” University of California, Santa Cruz, December 2007. Ceph is open source software designed to provide object, block and file storage from a distributed storage cluster. The storage cluster can be comprised of storage nodes with one or more memory elements (e.g., disks) for storing data. Storage nodes are also referred to as object storage devices (OSDs), which can be physical or logical storage elements. In Ceph, storage nodes generally include an object storage device (OSD) software or daemon, which actually stores data as objects on the storage nodes. Ceph OSD software typically stores data on a local filesystem including, but not limited to, a B-tree file system (Btrfs). At least one Ceph metadata server can be provided for a storage cluster to store metadata associated with the objects (e.g., inodes, directories, etc.). Ceph monitors are provided for monitoring active and failed storage nodes in the cluster. It should be understood that embodiments described herein could be implemented in Ceph, or potentially in other distributed storage systems.
A distributed storage system such as Ceph, can provide storage in a storage cluster for data from multiple tenants. Generally, in Ceph, objects from the tenants are pseudo-randomly distributed across the cluster and are monitored by the same set of storage processes (e.g., OSD daemons). Thus, the same global configurations and distribution settings for dividing objects between different nodes may be used to store the objects of the tenants.
In Ceph, how and where to store data in a cluster is determined by a Controlled Replication Under Scalable Hashing (CRUSH) algorithm that computes data storage locations based on a CRUSH map. The CRUSH map identifies information about the storage cluster including the layout and capacity of storage nodes and how redundancy should be managed. More specifically, the map can contain a list of rules that tells CRUSH how it should replicate data in a Ceph cluster's pool. The rules can contain a replication factor for a particular pool of data to help determine how many times the data is to be replicated within the cluster and on which storage nodes the replicated data is to be stored. A pool can comprise a collection of data, such as objects, and a replication factor can be assigned to each pool. Typically, a pool can be shared across tenants.
In a typical Ceph configuration, when multiple tenants use a shared storage cluster, the same set of bucket weights and CRUSH rules apply to all tenants, and objects belonging to different tenants share the same object storage device daemons and disk partitions. In some scenarios, however, a tenant may have different requirements or preferences for its data than other tenants sharing the same storage cluster in a distributed storage system. Such requirements may be related to performance, distribution, and/or replication in at least some scenarios. For example, a tenant may desire certain input/output operations to be satisfied that require storage on a particular type of disk. Another tenant may not need maximum priority for its data. In another example, a tenant may prefer particular storage nodes or disk partitions for its data. In addition, a multi-tenant resource isolation problem may exist when a disk partition contains data from multiple tenants. If the partition fails in this scenario, then all of the tenants sharing the partition could be affected by the failure and subsequent recovery process.
In accordance with at least one embodiment of the present disclosure, the aforementioned issues (and more) associated with existing distributed storage systems can be resolved. Embodiments of distributed storage system 100 enable tenant-specific sharding of storage disks in each storage node 60(1)-60(X) of storage cluster 50. Administration host 10 is configured to enable tenant-specific data storage modules to control data replication processes and primary node selection to provide unique, per tenant behaviors. In particular, one or more tenant-specific disk partitions may be allocated for each tenant sharing the storage cluster. The tenant-specific disk partitions can be controlled by individual per tenant data storage modules. This enables independent configurations per tenant for data storage in the storage cluster. A storage map can be generated with the configurations. In at least one embodiment, independent configurations for a tenant could include rules based, at least in part, on the tenant's subscription model to the distributed storage service. Other independent configurations for a tenant could include rules based on policies configured for differentiated services (e.g., performance, distribution, replication) for the tenant. In at least some embodiments, different sets of independent configurations for the same tenant may be created for different data storage modules controlling the multiple disk partitions. Accordingly, the tenant may be allowed to apply different sets of configurations to different sets of data.
Several advantages can be achieved by offering a distributed storage system that enables unique, per-tenant configurations for tenant data stored in a shared storage cluster. First, differentiated service levels can be provided to tenants based on a subscription model associated with each tenant. The differential behavior enabled for each tenant can provide the benefits of performance isolation between tenants and failure isolation between disk partitions. By enabling tenant-specific partitions per disk with a dedicated data storage module per tenant, a partition failure that occurs for a particular tenant can trigger a recovery process that impacts only that tenant during rebalancing and recreating replicas of data that is lost due to the failure. Also, by enabling differentiated settings for each data storage module per tenant, the performance impacts of each tenant can be isolated. Such performance impacts can occur during cluster rebalancing, failure recovery, and reading/writing data that relies on a replica storage node selection. Also, individual data storage modules per tenant prevent contention of a single data storage module between tenants sharing the data storage module.
It should be noted that, as used herein, ‘tenant’ is intended to refer to an entity (or an identifier or other representation of the entity) that is associated with certain data stored (or allowed to be stored) in a distributed storage system. The association between an entity and the stored data may be in the form of ownership, management, control, etc. of that data, which can include objects, files, blocks, etc. Generally, each object, block or file stored in a distributed storage system is associated with a single tenant. Multiple tenants may have data stored in the distributed storage system.
Turning to the infrastructure of
In network 5, network traffic, which is inclusive of packets, frames, signals, cells, datagrams, protocol data units (PDUs), data, etc., can be sent and received according to any suitable communication messaging protocols. Suitable communication messaging protocols can include a multi-layered scheme such as Open Systems Interconnection (OSI) model, or any derivations or variants thereof (e.g., Transmission Control Protocol/Internet Protocol (TCP/IP), user datagram protocol/IP (UDP/IP)). A packet is a unit of data for communicating information in a network, and can be routed between a source node (e.g., administration host 10, gateway 80) and a destination node (e.g., storage nodes 60(1)-60(X)) via network 5. A packet includes, but is not limited to, a source network address, a destination network address, and a payload containing the information to be communicated. By way of example, these network addresses can be Internet Protocol (IP) addresses in a TCP/IP messaging protocol. Information is generally represented by data and, as used herein, ‘data’ refers to any type of binary, numeric, voice, video, media, textual, or script data, or any type of source or object code, or any other suitable information in any appropriate format that may be communicated from one point to another in electronic devices and/or networks.
Administration host 10 and gateway 80 can be implemented as one or more network elements in a network environment. As used herein, the term ‘network element’ is meant to encompass servers, processors, modules, routers, switches, gateways, bridges, load balancers, firewalls, inline service nodes, proxies, or any other suitable device, component, element, proprietary appliance, or object operable to exchange information in a network environment. This network element may include any suitable hardware, software, components, modules, interfaces, or objects that facilitate the operations thereof. This may be inclusive of appropriate algorithms and communication protocols that allow for the effective exchange of data or information.
Storage nodes 60(1)-60(X) are network elements that include physical or logical storage elements with one or more disks for storing electronic data. In embodiments disclosed herein, tenant data is stored in storage nodes 60(1)-60(X). When the data is stored as objects, each object may have a unique identifier and associated metadata. Data storage modules may be provided in each storage node to determine storage locations for the data, to store the data, and to provide access to the data over the network. Data in storage nodes 60(1)-60(X) can be accessed by clients (not shown) via gateway 80 by an application programming interface (API) or hypertext transfer protocol (HTTP), for example. Clients can enable users, including human users and/or applications, to access the stored data.
In one implementation, network elements of
As shown in
Other tenant-level policies can be configured to enable differentiated services for the tenant. For example, such policies can include, but are not limited to, performance requirements, distribution requirements, and replication requirements. Performance requirements can be based on some performance characteristic of a disk such as speed of the disk, input/output rate of the disk, etc. Distribution requirements can be based on where data is to be stored such as a particular disk, a particular rack, a particular location, etc. Distribution requirements can also include how far apart or how close together replicas can be relative to each other. Replication requirements can be based on the number of replicas desired for the data of a tenant. By way of illustration, one tenant may have strict input/output performance requirements and thus, may choose a policy to ensure that most of its data and the primary replica nodes are in solid state device (SSD) disks. A different tenant may choose a different policy if that tenant does not have the same need for SSD-like throughputs.
In at least one embodiment, policies may be stored in policies repository 34. Policies repository 34 may be provided in any suitable type of storage, which may be internal to administration host 10 or external (entirely or in part). Internal storage could include any internal memory of administration host 10, such as static storage, random access memory (RAM), or cache, for example. External storage could include a network storage technique such as network attached storage (NAS) or storage area network (SAN), or memory of another network element.
Configuration module 16 may be provided in administration host 10 to enable per tenant configurations. In at least one embodiment, configuration module 16 and a user interface can enable a user to configure storage cluster 50 with individual per tenant data storage modules for tenant-specific disk partitions in storage nodes 60(1)-60(X). When a new data storage module is added for a tenant, a unique identifier may be generated for the data storage module and associated with the tenant. Per tenant configuration of a storage node is described in more detail with reference to
Turning to
Embodiments disclosed herein allow for multiple data storage modules per disk, as shown by data storage modules 70(1)-70(Y) of disk 20. At least one dedicated data storage module may be provided per tenant. For example, each data storage module of data storage modules 70(1)-70(Y) is associated with a single tenant. In some instances, more than one of the data storage modules 70(1)-70(Y) may be associated with the same tenant and assigned to different disk partitions within the same disk or across multiple disks. In at least one embodiment, however, none of these data storage modules is to be associated with multiple tenants.
For illustration purposes, assume N=3 such that three tenants store data in storage node 60(1), and Y=5 such that five data storage modules are configured in storage node 60(1). In this scenario, one possible result includes data storage modules 75(1) and 75(3) associated with tenant A 26(1), data storage modules 75(2) and 75(4) associated with tenant B 26(2), and data storage module 70(5) associated with tenant C 26(3). In addition, each data storage module 70(1)-70(5) could be assigned to a respective dedicated disk partition 22(1)-22(5).
Embodiments disclosed herein also enable tenant-level sharding. In the example shown in
Embodiments also allow a single disk to be shared by a fewer number of tenants. This may occur, for example, if one or more tenants require a significant amount of storage. In certain cases, an entire disk (e.g., 1 TB) may be allocated for use by a single tenant. Having a disk shared by a fewer number of tenants can minimize the risk of interruption by other tenants.
In at least one embodiment, tenant-level disk partitions may be selected manually or automatically. Configuration module 16 may be provided in administration host 10 to enable the selection. For a manual selection, a user may add a data storage module for a particular tenant and then manually select a particular disk partition to be allocated for the data storage module. Alternatively, the disk partition may be pre-determined based on policies. In this case, after adding a data storage module for a particular tenant, a disk partition may be automatically selected and allocated for the data storage module. In this scenario, the disk partition may be selected based on policies, such as the tenant's subscription model and/or tenant-specific policies to enable differentiated services for the tenant.
For illustration purposes, assume a subscription model specifies 1 TB of storage for tenant A, and a tenant-specific policy requires SSD disks for tenant A's data. In this example scenario, 1 TB of available space on a SSD disk in a storage node of the cluster may be automatically identified and allocated for the data storage module associated with tenant A. In another illustration with reference to
In at least one embodiment, an association is created between a data storage module and a disk partition that is selected and allocated for the data storage module. An association is also created between the data storage module and the tenant for which the data storage module was created. In addition, one or more rules for the tenant may be created based on policies configured for the tenant (e.g., subscription model, performance requirements, distribution requirements, replication factor, etc.). The rules may be associated with the tenant and the data storage module associated with that tenant. These associations may be realized in any suitable manner including, but not limited to, mapping a unique identifier of the data storage module to suitable identifiers or other representations of the disk partition, the tenant, and/or the rules.
In at least one embodiment, these mappings can be provided in storage map 32. Storage map generator 14 may be provided in administration host 10 to generate storage map 32. Storage map 32 can be used by data storage modules, including data storage modules 70(1)-70(Y), to determine how to store and retrieve data in a storage cluster such as storage cluster 50. In at least one embodiment, storage map 32 is a map of storage cluster 50, including at least a list of tenant-specific data storage modules (e.g., using their unique identifiers) mapped to associated tenants, allocated disk partitions, and sets of rules generated for the associated tenants. Because each data storage module is dedicated to a single tenant, the tenant can decide what policies to configure so that the rules that are generated enable a desired data distribution in the storage cluster.
At least one embodiment allows for a user to configure multiple sets of policies for a single tenant. Thus, multiple sets of rules can be generated for different data storage modules of the same tenant. For example, assume first and second data storage modules are associated with tenant A, and first and second rule sets are also associated with tenant A. In one possible scenario, the first rule set could be associated with the first data storage module and the second rule set could be associated with the second data storage module. Accordingly, the different data storage modules can be used for different types of data of tenant A. For example, the first and second data storage modules could be assigned to different types of disks. The first rule set could include a rule requiring an SSD disk partition, and the second rule set may not specify a particular type of disk and may rely on default settings or criteria. In this example scenario, critical data could be stored using the first data storage module (i.e., on an SSD disk) and less critical data could be stored using the second data storage module.
In one example implementation using a distributed storage system such as Ceph, an embodiment as described herein can allow for pools, which are logical groups for storing data in a storage cluster, to have a one-to-one correspondence to tenants. Users associated with a particular tenant may be authorized to access only pools corresponding to that particular tenant. The tenant's data to be added to the storage cluster is to be stored in the pool corresponding to the tenant. Rules that are created from the policies can be written for the pool belonging to the tenant, which uses one or more data storage modules that are only mapped to that tenant. The rules can be provided in the storage map and can be used by the data storage modules to determine a primary storage location for the data and its replicas in the storage cluster.
Turning to
At 302, administration host 10 receives an indication of a type of subscription model for a tenant. The type of the subscription model may be selected for the tenant by a user via a user interface. The subscription model may specify a partition size (e.g., 100 GB, 500 GB, 1 TB, etc.) desired by the tenant for storing its data. The subscription model may also specify a priority relative to other subscription types. Priority could be used, for example, to resolve contention between data storage modules accessing the same disk.
At 304, administration host 10 receives an indication of policies to be applied to data of the tenant. The policies may be configured by the user via a user interface. Policies may include, for example, performance requirements, distribution requirements, replication factor, etc. preferred by the tenant. In some embodiments, any of the performance, distribution and/or replication requirements may be included in a subscription model rather than being configured separately. At 306, administration host 10 receives an indication of a data storage module to be assigned to the tenant. The data storage module may be assigned to the tenant by the user via a user interface, and may be dedicated to that tenant. At 308, a unique identifier (UID) may be generated for the data storage module assigned to the tenant.
A disk partition for the data storage module may be selected manually or automatically. For manual selection at 312, administration host 10 can receive an indication of a particular disk partition (or an entire disk) to be allocated for the tenant. The particular disk partition may be selected by the user via the user interface. At 314, the selected disk partition may be allocated for the tenant. For automatic selection of a disk partition (or entire disk), at 310, administration host 10 can identify and allocate a disk partition (or an entire disk) from available storage nodes in a storage cluster based on policies configured for the tenant and/or the subscription model assigned to the tenant.
At 316, an association is created between the data storage module and the allocated disk partition. In at least one embodiment, this association may be realized by mapping the UID of the data storage module to the disk partition. In an example, a suitable identifier or other representation of the disk partition (or disk) may be used for the mapping. At 318, an association is created between the data storage module and the tenant. In at least one embodiment, this association may be realized by mapping the UID of the data storage module to the tenant. In an example, a suitable identifier or other representation of the tenant may be used for the mapping. At 320, one or more rules can be created based on policies configured for the tenant (e.g., replication factor, subscription model, performance requirements, distribution requirements, etc.). The rules may be associated with the data storage module that is associated with the tenant. This association may be realized by mapping the UID of the data storage module to the rules. In an example, a suitable identifier or other representation of the rules may be used for the mapping. In at least one embodiment, these mappings can be provided in a storage map used by data storage module to determine how to store and retrieve data in the storage cluster. In addition, the rules created from the policies may also be provided in the storage map in at least one embodiment.
Turning to
Initially, an authorized user of a particular tenant may access gateway 80 in order to add data to the storage cluster. In at least one implementation (e.g., in Ceph), data is added to a pool corresponding to the tenant. At 404, gateway 80 may receive a request for data of the tenant to be stored in storage cluster 50 of distributed storage system 100. In at least one embodiment, the request may be an indication that the authorized user (e.g., human user or application) has stored objects or other data in a pool corresponding to the tenant. At 406, the tenant associated with the data can be identified based on the pool in which the data is stored. At 408, a data storage module associated with the tenant is identified. This identification may be made based on a mapping of a unique identifier of the data storage module to the tenant.
At 410, the identified data storage module (or modules) associated with the tenant can be run to determine how and where to store the data based on a storage map. Rules associated with the data storage module can be determined from the storage map and used to calculate how and where to store the data (e.g., which disk partition to use, how many replicas to store, where to store the replicas, etc.). Thus, tenant-specific data storage modules can control the primary node selection and the data replication process, which enables unique tenant behaviors configured by policies. Moreover, the dedicated, tenant-specific disk partitions enable failure and performance isolation relative to other tenants and their dedicated disk partitions.
Variations and Implementations
Note that, as used herein, unless expressly stated to the contrary, use of the phrase ‘at least one of’ refers to any combination of the named items, elements, conditions, activities, etc. For example, ‘at least one of X, Y, and Z’ is intended to mean any of the following: 1) one or more X's, but not Y and not Z; 2) one or more Y's, but not X and not Z; 3) one or more Z's, but not X and not Y; 4) one or more X's and one or more Y's, but not Z; 5) one or more X's and one or more Z's, but not Y; 6) one or more Y's and one or more Z's, but not X; or 7) one or more X's, one or more Y's, and one or more Z's.
In certain example implementations the tenant-level configuration functions for a distributed storage system outlined herein may be implemented by logic encoded in one or more machine readable storage media (e.g., embedded logic provided in an application specific integrated circuit (ASIC), digital signal processor (DSP) instructions, software (potentially inclusive of object code and source code) to be executed by a processor or other similar machine, etc.). In some of these instances, a memory element (e.g., memory elements 17, 67, a memory element of gateway 80) can store data used for the operations described herein. This includes the memory element being able to store software, logic, code, or processor instructions that are executed to carry out the activities described in this Specification. A processor can execute any type of instructions associated with the data to achieve the operations detailed herein. In one example, the processor (e.g., processors 19, 69, a processor of gateway 80) could transform an element or an article (e.g., data) from one state or thing to another state or thing. In another example, the activities outlined herein may be implemented with fixed logic or programmable logic (e.g., software/computer instructions executed by a processor) and the elements identified herein could be some type of a programmable processor, programmable digital logic (e.g., a field programmable gate array (FPGA), an erasable programmable read only memory (EPROM), an electrically erasable programmable ROM (EEPROM)) or an ASIC that includes digital logic, software, code, electronic instructions, or any suitable combination thereof.
In one example implementation, administration host 10 may include software in order to achieve at least some of the tenant-level configuration functions outlined herein. These activities can be facilitated by policy module 12, storage map generator 14, and configuration module 16 (where the functionality of these modules can be suitably combined or divided in any appropriate manner, which may be based on particular configuration and/or provisioning needs). Administration host 10 can include memory elements (e.g., memory element 17) for storing information to be used in achieving at least some of the tenant-level configuration activities, as discussed herein. Additionally, administration host 10 may include one or more processors (e.g., processor 19) that can execute software or an algorithm to perform the tenant-level configuration operations, as disclosed in this Specification. These devices may further keep information in any suitable memory elements (e.g., random access memory (RAM), ROM, EPROM, EEPROM, ASIC, etc.), software, hardware, or in any other suitable component, device, element, or object where appropriate and based on particular needs. Any of the memory items discussed herein (e.g., object, block, file, database, tables, trees, cache, repository, etc.) should be construed as being encompassed within the broad term ‘memory element.’ Similarly, any of the potential processing elements, modules, and machines described in this Specification should be construed as being encompassed within the broad term ‘processor.’ Administration host 10 can also include suitable interfaces (e.g., network interface card) for receiving, transmitting, and/or otherwise communicating data or information in distributed storage system 100.
Note that with the example provided above, as well as numerous other examples provided herein, interaction may be described in terms of two, three, or four network elements. However, this has been done for purposes of clarity and example only. In certain cases, it may be easier to describe one or more of the functionalities of a given set of operations by only referencing a limited number of network elements and nodes. It should be appreciated that distributed storage system 100 (and its teachings) is readily scalable and can accommodate a large number of components, as well as more complicated/sophisticated arrangements and configurations. Accordingly, the examples provided should not limit the scope or inhibit the broad teachings of distributed storage system 100 as potentially applied to a myriad of other architectures.
Although the present disclosure has been described in detail with reference to particular arrangements and configurations, these example configurations and arrangements may be changed significantly without departing from the scope of the present disclosure. For example, although the present disclosure has been described with reference to particular tenant-level configuration functions (e.g., applied in a Ceph storage system), these tenant-level configuration functions may be applicable in other distributed storage systems. Also, while the tenant-level configuration functions are particularly suited to distributed storage systems that store data in the form of objects, the teachings herein may also be applied to distributed storage systems that store data in various other types of structures including, but not limited to, files and blocks.
Finally, it is also important to note that the operations in the preceding flowcharts illustrate only some of the possible scenarios and patterns that may be executed in association with addressing tenant configuration operations in a distributed storage system. Some of these operations may be deleted, removed, combined, or divided where appropriate, or may be modified or changed considerably without departing from the scope of the present disclosure. In addition, a number of these operations have been described as being executed before, after, concurrently with, or in parallel to, one or more additional operations. However, the timing of these operations may be altered considerably. The preceding operational flows have been offered for purposes of example and discussion. Distributed storage system 100, including administration host 10, may provide substantial flexibility in that any suitable arrangements, chronologies, configurations, and timing mechanisms may be provided without departing from the teachings of the present disclosure.
Number | Name | Date | Kind |
---|---|---|---|
4688695 | Hirohata | Aug 1987 | A |
5263003 | Cowles et al. | Nov 1993 | A |
5339445 | Gasztonyi | Aug 1994 | A |
5430859 | Norman et al. | Jul 1995 | A |
5457746 | Dolphin | Oct 1995 | A |
5535336 | Smith et al. | Jul 1996 | A |
5588012 | Oizumi | Dec 1996 | A |
5617421 | Chin et al. | Apr 1997 | A |
5680579 | Young et al. | Oct 1997 | A |
5690194 | Parker et al. | Nov 1997 | A |
5740171 | Mazzola et al. | Apr 1998 | A |
5742604 | Edsall et al. | Apr 1998 | A |
5764636 | Edsall | Jun 1998 | A |
5809285 | Hilland | Sep 1998 | A |
5812814 | Sukegawa | Sep 1998 | A |
5812950 | Tom | Sep 1998 | A |
5838970 | Thomas | Nov 1998 | A |
5999930 | Wolff | Dec 1999 | A |
6035105 | McCloghrie et al. | Mar 2000 | A |
6043777 | Bergman et al. | Mar 2000 | A |
6101497 | Ofek | Aug 2000 | A |
6148414 | Brown et al. | Nov 2000 | A |
6185203 | Berman | Feb 2001 | B1 |
6188694 | Fine et al. | Feb 2001 | B1 |
6202135 | Kedem et al. | Mar 2001 | B1 |
6208649 | Kloth | Mar 2001 | B1 |
6209059 | Ofer et al. | Mar 2001 | B1 |
6219699 | McCloghrie et al. | Apr 2001 | B1 |
6219753 | Richardson | Apr 2001 | B1 |
6223250 | Yokono | Apr 2001 | B1 |
6226771 | Hilla et al. | May 2001 | B1 |
6260120 | Blumenau et al. | Jul 2001 | B1 |
6266705 | Ullum et al. | Jul 2001 | B1 |
6269381 | St. Pierre et al. | Jul 2001 | B1 |
6269431 | Dunham | Jul 2001 | B1 |
6295575 | Blumenau et al. | Sep 2001 | B1 |
6400730 | Latif et al. | Jun 2002 | B1 |
6408406 | Parris | Jun 2002 | B1 |
6542909 | Tamer et al. | Apr 2003 | B1 |
6542961 | Matsunami et al. | Apr 2003 | B1 |
6553390 | Gross et al. | Apr 2003 | B1 |
6564252 | Hickman | May 2003 | B1 |
6647474 | Yanai et al. | Nov 2003 | B2 |
6675258 | Bramhall et al. | Jan 2004 | B1 |
6683883 | Czeiger et al. | Jan 2004 | B1 |
6694413 | Mimatsu et al. | Feb 2004 | B1 |
6708227 | Cabrera et al. | Mar 2004 | B1 |
6715007 | Williams et al. | Mar 2004 | B1 |
6728791 | Young | Apr 2004 | B1 |
6772231 | Reuter et al. | Aug 2004 | B2 |
6820099 | Huber et al. | Nov 2004 | B1 |
6847647 | Wrenn | Jan 2005 | B1 |
6848759 | Doornbos et al. | Feb 2005 | B2 |
6850955 | Sonoda et al. | Feb 2005 | B2 |
6876656 | Brewer et al. | Apr 2005 | B2 |
6880062 | Ibrahim et al. | Apr 2005 | B1 |
6898670 | Nahum | May 2005 | B2 |
6907419 | Pesola et al. | Jun 2005 | B1 |
6912668 | Brown et al. | Jun 2005 | B1 |
6952734 | Gunlock et al. | Oct 2005 | B1 |
6976090 | Ben-Shaul et al. | Dec 2005 | B2 |
6978300 | Beukema et al. | Dec 2005 | B1 |
6983303 | Pellegrino et al. | Jan 2006 | B2 |
6986015 | Testardi | Jan 2006 | B2 |
6986069 | Oehler et al. | Jan 2006 | B2 |
7051056 | Rodriguez-Rivera et al. | May 2006 | B2 |
7069465 | Chu et al. | Jun 2006 | B2 |
7073017 | Yamamoto | Jul 2006 | B2 |
7108339 | Berger | Sep 2006 | B2 |
7149858 | Kiselev | Dec 2006 | B1 |
7171514 | Coronado et al. | Jan 2007 | B2 |
7171668 | Molloy et al. | Jan 2007 | B2 |
7174354 | Andreasson | Feb 2007 | B2 |
7200144 | Terrell et al. | Apr 2007 | B2 |
7222255 | Claessens et al. | May 2007 | B1 |
7237045 | Beckmann et al. | Jun 2007 | B2 |
7240188 | Takata et al. | Jul 2007 | B2 |
7246260 | Brown et al. | Jul 2007 | B2 |
7266718 | Idei et al. | Sep 2007 | B2 |
7269168 | Roy et al. | Sep 2007 | B2 |
7277431 | Walter et al. | Oct 2007 | B2 |
7277948 | Igarashi et al. | Oct 2007 | B2 |
7305658 | Hamilton | Dec 2007 | B1 |
7328434 | Swanson et al. | Feb 2008 | B2 |
7340555 | Ashmore et al. | Mar 2008 | B2 |
7346751 | Prahlad et al. | Mar 2008 | B2 |
7352706 | Klotz et al. | Apr 2008 | B2 |
7353305 | Pangal et al. | Apr 2008 | B2 |
7359321 | Sindhu et al. | Apr 2008 | B1 |
7383381 | Faulkner et al. | Jun 2008 | B1 |
7403987 | Marinelli et al. | Jul 2008 | B1 |
7433326 | Desai et al. | Oct 2008 | B2 |
7433948 | Edsall | Oct 2008 | B2 |
7434105 | Rodriguez-Rivera et al. | Oct 2008 | B1 |
7441154 | Klotz et al. | Oct 2008 | B2 |
7447839 | Uppala | Nov 2008 | B2 |
7487321 | Muthiah et al. | Feb 2009 | B2 |
7500053 | Kavuri et al. | Mar 2009 | B1 |
7512744 | Banga et al. | Mar 2009 | B2 |
7542681 | Cornell et al. | Jun 2009 | B2 |
7558872 | Senevirathne et al. | Jul 2009 | B1 |
7587570 | Sarkar et al. | Sep 2009 | B2 |
7631023 | Kaiser et al. | Dec 2009 | B1 |
7643505 | Colloff | Jan 2010 | B1 |
7654625 | Amann et al. | Feb 2010 | B2 |
7657796 | Kaiser et al. | Feb 2010 | B1 |
7668981 | Nagineni et al. | Feb 2010 | B1 |
7669071 | Cochran et al. | Feb 2010 | B2 |
7689384 | Becker | Mar 2010 | B1 |
7694092 | Mizuno | Apr 2010 | B2 |
7697554 | Ofer et al. | Apr 2010 | B1 |
7706303 | Bose et al. | Apr 2010 | B2 |
7707481 | Kirschner et al. | Apr 2010 | B2 |
7716648 | Vaidyanathan et al. | May 2010 | B2 |
7752360 | Galles | Jul 2010 | B2 |
7757059 | Ofer et al. | Jul 2010 | B1 |
7774329 | Peddy | Aug 2010 | B1 |
7774839 | Nazzal | Aug 2010 | B2 |
7793138 | Rastogi et al. | Sep 2010 | B2 |
7840730 | D'Amato et al. | Nov 2010 | B2 |
7843906 | Chidambaram et al. | Nov 2010 | B1 |
7895428 | Boland, IV et al. | Feb 2011 | B2 |
7904599 | Bennett | Mar 2011 | B1 |
7930494 | Goheer et al. | Apr 2011 | B1 |
7975175 | Votta et al. | Jul 2011 | B2 |
7979670 | Saliba et al. | Jul 2011 | B2 |
7984259 | English | Jul 2011 | B1 |
8031703 | Gottumukkula et al. | Oct 2011 | B2 |
8032621 | Upalekar et al. | Oct 2011 | B1 |
8051197 | Mullendore et al. | Nov 2011 | B2 |
8086755 | Duffy, IV et al. | Dec 2011 | B2 |
8161134 | Mishra et al. | Apr 2012 | B2 |
8196018 | Forhan et al. | Jun 2012 | B2 |
8205951 | Boks | Jun 2012 | B2 |
8218538 | Chidambaram et al. | Jul 2012 | B1 |
8230066 | Heil | Jul 2012 | B2 |
8234377 | Cohn | Jul 2012 | B2 |
8266238 | Zimmer et al. | Sep 2012 | B2 |
8272104 | Chen et al. | Sep 2012 | B2 |
8274993 | Sharma et al. | Sep 2012 | B2 |
8290919 | Kelly | Oct 2012 | B1 |
8297722 | Chambers et al. | Oct 2012 | B2 |
8301746 | Head et al. | Oct 2012 | B2 |
8335231 | Kloth et al. | Dec 2012 | B2 |
8341121 | Claudatos et al. | Dec 2012 | B1 |
8345692 | Smith | Jan 2013 | B2 |
8352941 | Protopopov | Jan 2013 | B1 |
8392760 | Kandula et al. | Mar 2013 | B2 |
8442059 | de la Iglesia et al. | May 2013 | B1 |
8479211 | Marshall | Jul 2013 | B1 |
8495356 | Ashok et al. | Jul 2013 | B2 |
8514868 | Hill | Aug 2013 | B2 |
8532108 | Li et al. | Sep 2013 | B2 |
8560663 | Baucke et al. | Oct 2013 | B2 |
8619599 | Even | Dec 2013 | B1 |
8626891 | Guru | Jan 2014 | B2 |
8630983 | Sengupta et al. | Jan 2014 | B2 |
8660129 | Brendel et al. | Feb 2014 | B1 |
8661299 | Ip | Feb 2014 | B1 |
8677485 | Sharma et al. | Mar 2014 | B2 |
8683296 | Anderson et al. | Mar 2014 | B2 |
8706772 | Hartig | Apr 2014 | B2 |
8719804 | Jain | May 2014 | B2 |
8725854 | Edsall | May 2014 | B2 |
8768981 | Milne | Jul 2014 | B1 |
8775773 | Acharya | Jul 2014 | B2 |
8793372 | Ashok et al. | Jul 2014 | B2 |
8805918 | Chandrasekaran et al. | Aug 2014 | B1 |
8805951 | Faibish et al. | Aug 2014 | B1 |
8832330 | Lancaster | Sep 2014 | B1 |
8855116 | Rosset et al. | Oct 2014 | B2 |
8856339 | Mestery et al. | Oct 2014 | B2 |
8868474 | Leung et al. | Oct 2014 | B2 |
8887286 | Dupont et al. | Nov 2014 | B2 |
8898385 | Jayaraman et al. | Nov 2014 | B2 |
8909928 | Ahmad et al. | Dec 2014 | B2 |
8918510 | Gmach et al. | Dec 2014 | B2 |
8918586 | Todd et al. | Dec 2014 | B1 |
8924720 | Raghuram et al. | Dec 2014 | B2 |
8930747 | Levijarvi et al. | Jan 2015 | B2 |
8935500 | Gulati et al. | Jan 2015 | B1 |
8949677 | Brundage et al. | Feb 2015 | B1 |
8996837 | Bono | Mar 2015 | B1 |
9003086 | Schuller et al. | Apr 2015 | B1 |
9007922 | Mittal et al. | Apr 2015 | B1 |
9009427 | Sharma et al. | Apr 2015 | B2 |
9009704 | McGrath | Apr 2015 | B2 |
9053167 | Swift | Jun 2015 | B1 |
9075638 | Barnett et al. | Jul 2015 | B2 |
9141554 | Candelaria | Sep 2015 | B1 |
9141785 | Mukkara | Sep 2015 | B2 |
9164795 | Vincent | Oct 2015 | B1 |
9176677 | Fradkin | Nov 2015 | B1 |
9201704 | Chang et al. | Dec 2015 | B2 |
9203784 | Chang et al. | Dec 2015 | B2 |
9207882 | Rosset et al. | Dec 2015 | B2 |
9207929 | Katsura | Dec 2015 | B2 |
9213612 | Candelaria | Dec 2015 | B2 |
9223564 | Munireddy et al. | Dec 2015 | B2 |
9223634 | Chang et al. | Dec 2015 | B2 |
9244761 | Yekhanin et al. | Jan 2016 | B2 |
9250969 | Lager-Cavilla et al. | Feb 2016 | B2 |
9264494 | Factor et al. | Feb 2016 | B2 |
9270754 | Iyengar et al. | Feb 2016 | B2 |
9280487 | Candelaria | Mar 2016 | B2 |
9304815 | Vasanth et al. | Apr 2016 | B1 |
9313048 | Chang et al. | Apr 2016 | B2 |
9374270 | Nakil et al. | Jun 2016 | B2 |
9378060 | Jansson et al. | Jun 2016 | B2 |
9396251 | Boudreau et al. | Jul 2016 | B1 |
9448877 | Candelaria | Sep 2016 | B2 |
9471348 | Zuo et al. | Oct 2016 | B2 |
9501473 | Kong et al. | Nov 2016 | B1 |
9503523 | Rosset et al. | Nov 2016 | B2 |
9565110 | Mullendore et al. | Feb 2017 | B2 |
9575828 | Agarwal et al. | Feb 2017 | B2 |
9582377 | Dhoolam et al. | Feb 2017 | B1 |
9614763 | Dong et al. | Apr 2017 | B2 |
9658868 | Hill | May 2017 | B2 |
9658876 | Chang et al. | May 2017 | B2 |
9733868 | Chandrasekaran et al. | Aug 2017 | B2 |
9763518 | Charest et al. | Sep 2017 | B2 |
9830240 | George et al. | Nov 2017 | B2 |
9853873 | Dasu et al. | Dec 2017 | B2 |
20020049980 | Hoang | Apr 2002 | A1 |
20020053009 | Selkirk et al. | May 2002 | A1 |
20020073276 | Howard et al. | Jun 2002 | A1 |
20020083120 | Soltis | Jun 2002 | A1 |
20020095547 | Watanabe et al. | Jul 2002 | A1 |
20020103889 | Markson et al. | Aug 2002 | A1 |
20020103943 | Lo et al. | Aug 2002 | A1 |
20020112113 | Karpoff et al. | Aug 2002 | A1 |
20020120741 | Webb et al. | Aug 2002 | A1 |
20020138675 | Mann | Sep 2002 | A1 |
20020156971 | Jones et al. | Oct 2002 | A1 |
20030023885 | Potter et al. | Jan 2003 | A1 |
20030026267 | Oberman et al. | Feb 2003 | A1 |
20030055933 | Ishizaki | Mar 2003 | A1 |
20030056126 | O'Connor et al. | Mar 2003 | A1 |
20030065986 | Fraenkel et al. | Apr 2003 | A1 |
20030084359 | Bresniker et al. | May 2003 | A1 |
20030118053 | Edsall et al. | Jun 2003 | A1 |
20030131105 | Czeiger et al. | Jul 2003 | A1 |
20030131165 | Asano | Jul 2003 | A1 |
20030131182 | Kumar et al. | Jul 2003 | A1 |
20030140134 | Swanson et al. | Jul 2003 | A1 |
20030140210 | Testardi | Jul 2003 | A1 |
20030149763 | Heitman et al. | Aug 2003 | A1 |
20030154271 | Baldwin et al. | Aug 2003 | A1 |
20030159058 | Eguchi et al. | Aug 2003 | A1 |
20030174725 | Shankar | Sep 2003 | A1 |
20030189395 | Doornbos et al. | Oct 2003 | A1 |
20030210686 | Terrell et al. | Nov 2003 | A1 |
20040024961 | Cochran et al. | Feb 2004 | A1 |
20040030857 | Krakirian et al. | Feb 2004 | A1 |
20040039939 | Cox et al. | Feb 2004 | A1 |
20040054776 | Klotz et al. | Mar 2004 | A1 |
20040057389 | Klotz et al. | Mar 2004 | A1 |
20040059807 | Klotz et al. | Mar 2004 | A1 |
20040088574 | Walter et al. | May 2004 | A1 |
20040117438 | Considine et al. | Jun 2004 | A1 |
20040123029 | Dalal | Jun 2004 | A1 |
20040128470 | Hetzler et al. | Jul 2004 | A1 |
20040128540 | Roskind | Jul 2004 | A1 |
20040153863 | Klotz et al. | Aug 2004 | A1 |
20040190901 | Fang | Sep 2004 | A1 |
20040215749 | Tsao | Oct 2004 | A1 |
20040230848 | Mayo et al. | Nov 2004 | A1 |
20040250034 | Yagawa et al. | Dec 2004 | A1 |
20050033936 | Nakano | Feb 2005 | A1 |
20050036499 | Dutt et al. | Feb 2005 | A1 |
20050050211 | Kaul et al. | Mar 2005 | A1 |
20050050270 | Horn et al. | Mar 2005 | A1 |
20050053073 | Kloth et al. | Mar 2005 | A1 |
20050055428 | Terai et al. | Mar 2005 | A1 |
20050060574 | Klotz et al. | Mar 2005 | A1 |
20050060598 | Klotz et al. | Mar 2005 | A1 |
20050071851 | Opheim | Mar 2005 | A1 |
20050076113 | Klotz et al. | Apr 2005 | A1 |
20050091426 | Horn et al. | Apr 2005 | A1 |
20050114611 | Durham et al. | May 2005 | A1 |
20050114615 | Ogasawara et al. | May 2005 | A1 |
20050117522 | Basavaiah et al. | Jun 2005 | A1 |
20050117562 | Wrenn | Jun 2005 | A1 |
20050138287 | Ogasawara et al. | Jun 2005 | A1 |
20050169188 | Cometto et al. | Aug 2005 | A1 |
20050185597 | Le et al. | Aug 2005 | A1 |
20050188170 | Yamamoto | Aug 2005 | A1 |
20050198523 | Shanbhag et al. | Sep 2005 | A1 |
20050235072 | Smith et al. | Oct 2005 | A1 |
20050283658 | Clark et al. | Dec 2005 | A1 |
20060015861 | Takata et al. | Jan 2006 | A1 |
20060015928 | Setty et al. | Jan 2006 | A1 |
20060034302 | Peterson | Feb 2006 | A1 |
20060045021 | Deragon et al. | Mar 2006 | A1 |
20060075191 | Lolayekar et al. | Apr 2006 | A1 |
20060098672 | Schzukin et al. | May 2006 | A1 |
20060117099 | Mogul | Jun 2006 | A1 |
20060136684 | Le et al. | Jun 2006 | A1 |
20060184287 | Belady et al. | Aug 2006 | A1 |
20060198319 | Schondelmayer et al. | Sep 2006 | A1 |
20060215297 | Kikuchi | Sep 2006 | A1 |
20060230227 | Ogasawara et al. | Oct 2006 | A1 |
20060242332 | Johnsen et al. | Oct 2006 | A1 |
20060251111 | Kloth et al. | Nov 2006 | A1 |
20070005297 | Beresniewicz et al. | Jan 2007 | A1 |
20070067593 | Satoyama et al. | Mar 2007 | A1 |
20070079068 | Draggon | Apr 2007 | A1 |
20070091903 | Atkinson | Apr 2007 | A1 |
20070094465 | Sharma et al. | Apr 2007 | A1 |
20070101202 | Garbow | May 2007 | A1 |
20070121519 | Cuni et al. | May 2007 | A1 |
20070136541 | Herz et al. | Jun 2007 | A1 |
20070162969 | Becker | Jul 2007 | A1 |
20070211640 | Palacharla et al. | Sep 2007 | A1 |
20070214316 | Kim | Sep 2007 | A1 |
20070250838 | Belady et al. | Oct 2007 | A1 |
20070258380 | Chamdani et al. | Nov 2007 | A1 |
20070263545 | Foster et al. | Nov 2007 | A1 |
20070276884 | Hara et al. | Nov 2007 | A1 |
20070283059 | Ho et al. | Dec 2007 | A1 |
20080016412 | White et al. | Jan 2008 | A1 |
20080034149 | Sheen | Feb 2008 | A1 |
20080052459 | Chang et al. | Feb 2008 | A1 |
20080059698 | Kabir et al. | Mar 2008 | A1 |
20080114933 | Ogasawara et al. | May 2008 | A1 |
20080126509 | Subrannanian et al. | May 2008 | A1 |
20080126734 | Murase | May 2008 | A1 |
20080168304 | Flynn et al. | Jul 2008 | A1 |
20080201616 | Ashmore | Aug 2008 | A1 |
20080244184 | Lewis | Oct 2008 | A1 |
20080256082 | Davies | Oct 2008 | A1 |
20080267217 | Colville et al. | Oct 2008 | A1 |
20080288661 | Galles | Nov 2008 | A1 |
20080294888 | Ando et al. | Nov 2008 | A1 |
20090063766 | Matsumura et al. | Mar 2009 | A1 |
20090083484 | Basham et al. | Mar 2009 | A1 |
20090089567 | Boland, IV et al. | Apr 2009 | A1 |
20090094380 | Qiu et al. | Apr 2009 | A1 |
20090094664 | Butler et al. | Apr 2009 | A1 |
20090125694 | Innan et al. | May 2009 | A1 |
20090193223 | Saliba et al. | Jul 2009 | A1 |
20090201926 | Kagan et al. | Aug 2009 | A1 |
20090222733 | Basham et al. | Sep 2009 | A1 |
20090240873 | Yu et al. | Sep 2009 | A1 |
20090282471 | Green et al. | Nov 2009 | A1 |
20090323706 | Germain et al. | Dec 2009 | A1 |
20100011365 | Gerovac et al. | Jan 2010 | A1 |
20100030995 | Wang | Feb 2010 | A1 |
20100046378 | Knapp et al. | Feb 2010 | A1 |
20100083055 | Ozonat | Apr 2010 | A1 |
20100174968 | Charles et al. | Jul 2010 | A1 |
20100318609 | Lahiri et al. | Dec 2010 | A1 |
20100318837 | Murphy et al. | Dec 2010 | A1 |
20110010394 | Carew | Jan 2011 | A1 |
20110022691 | Banerjee et al. | Jan 2011 | A1 |
20110029824 | Schöler et al. | Feb 2011 | A1 |
20110035494 | Pandey et al. | Feb 2011 | A1 |
20110075667 | Li et al. | Mar 2011 | A1 |
20110087848 | Trent | Apr 2011 | A1 |
20110119556 | de Buen | May 2011 | A1 |
20110142053 | Van Der Merwe | Jun 2011 | A1 |
20110161496 | Nicklin | Jun 2011 | A1 |
20110173303 | Rider | Jul 2011 | A1 |
20110228679 | Varma et al. | Sep 2011 | A1 |
20110231899 | Pulier et al. | Sep 2011 | A1 |
20110239039 | Dieffenbach et al. | Sep 2011 | A1 |
20110252274 | Kawaguchi et al. | Oct 2011 | A1 |
20110255540 | Mizrahi et al. | Oct 2011 | A1 |
20110276584 | Cotner | Nov 2011 | A1 |
20110276675 | Singh et al. | Nov 2011 | A1 |
20110276951 | Jain | Nov 2011 | A1 |
20110299539 | Rajagopal et al. | Dec 2011 | A1 |
20110307450 | Hahn | Dec 2011 | A1 |
20110313973 | Srivas et al. | Dec 2011 | A1 |
20120023319 | Chin | Jan 2012 | A1 |
20120030401 | Cowan et al. | Feb 2012 | A1 |
20120042162 | Anglin | Feb 2012 | A1 |
20120054367 | Ramakrishnan et al. | Mar 2012 | A1 |
20120072578 | Alam | Mar 2012 | A1 |
20120072985 | Davne et al. | Mar 2012 | A1 |
20120075999 | Ko et al. | Mar 2012 | A1 |
20120084445 | Brock et al. | Apr 2012 | A1 |
20120084782 | Chou et al. | Apr 2012 | A1 |
20120096134 | Suit | Apr 2012 | A1 |
20120130874 | Mane | May 2012 | A1 |
20120131174 | Ferris et al. | May 2012 | A1 |
20120134672 | Banerjee | May 2012 | A1 |
20120144014 | Natham et al. | Jun 2012 | A1 |
20120159112 | Tokusho et al. | Jun 2012 | A1 |
20120167094 | Suit | Jun 2012 | A1 |
20120173581 | Hartig | Jul 2012 | A1 |
20120173589 | Kwon | Jul 2012 | A1 |
20120177039 | Berman | Jul 2012 | A1 |
20120177041 | Berman | Jul 2012 | A1 |
20120177042 | Berman | Jul 2012 | A1 |
20120177043 | Berman | Jul 2012 | A1 |
20120177044 | Berman | Jul 2012 | A1 |
20120177045 | Berman | Jul 2012 | A1 |
20120177370 | Berman | Jul 2012 | A1 |
20120179909 | Sagi et al. | Jul 2012 | A1 |
20120201138 | Yu et al. | Aug 2012 | A1 |
20120210041 | Flynn et al. | Aug 2012 | A1 |
20120254440 | Wang | Oct 2012 | A1 |
20120257501 | Kucharczyk | Oct 2012 | A1 |
20120265976 | Spiers et al. | Oct 2012 | A1 |
20120281706 | Agarwal et al. | Nov 2012 | A1 |
20120297088 | Wang et al. | Nov 2012 | A1 |
20120303618 | Dutta | Nov 2012 | A1 |
20120311106 | Morgan | Dec 2012 | A1 |
20120311568 | Jansen | Dec 2012 | A1 |
20120320788 | Venkataramanan et al. | Dec 2012 | A1 |
20120324114 | Dutta et al. | Dec 2012 | A1 |
20120331119 | Bose et al. | Dec 2012 | A1 |
20130003737 | Sinicrope | Jan 2013 | A1 |
20130013664 | Baird et al. | Jan 2013 | A1 |
20130028135 | Berman | Jan 2013 | A1 |
20130036212 | Jibbe et al. | Feb 2013 | A1 |
20130036213 | Hasan et al. | Feb 2013 | A1 |
20130036449 | Mukkara | Feb 2013 | A1 |
20130054888 | Bhat | Feb 2013 | A1 |
20130061089 | Valiyaparambil et al. | Mar 2013 | A1 |
20130067162 | Jayaraman et al. | Mar 2013 | A1 |
20130080823 | Roth et al. | Mar 2013 | A1 |
20130086340 | Fleming | Apr 2013 | A1 |
20130100858 | Kamath et al. | Apr 2013 | A1 |
20130111540 | Sabin | May 2013 | A1 |
20130138816 | Kuo et al. | May 2013 | A1 |
20130138836 | Cohen et al. | May 2013 | A1 |
20130139138 | Kakos | May 2013 | A1 |
20130144933 | Hinni et al. | Jun 2013 | A1 |
20130152076 | Patel | Jun 2013 | A1 |
20130152175 | Hromoko et al. | Jun 2013 | A1 |
20130163426 | Beliveau et al. | Jun 2013 | A1 |
20130163606 | Bagepalli et al. | Jun 2013 | A1 |
20130179941 | McGloin et al. | Jul 2013 | A1 |
20130182712 | Aguayo et al. | Jul 2013 | A1 |
20130185433 | Zhu et al. | Jul 2013 | A1 |
20130191106 | Kephart et al. | Jul 2013 | A1 |
20130198730 | Munireddy et al. | Aug 2013 | A1 |
20130208888 | Agrawal et al. | Aug 2013 | A1 |
20130212130 | Rahnama | Aug 2013 | A1 |
20130223236 | Dickey | Aug 2013 | A1 |
20130238641 | Mandelstein | Sep 2013 | A1 |
20130266307 | Garg et al. | Oct 2013 | A1 |
20130268922 | Tiwari et al. | Oct 2013 | A1 |
20130275470 | Cao | Oct 2013 | A1 |
20130297655 | Narasayya | Nov 2013 | A1 |
20130297769 | Chang et al. | Nov 2013 | A1 |
20130318134 | Bolik et al. | Nov 2013 | A1 |
20130318288 | Khan et al. | Nov 2013 | A1 |
20140006708 | Huynh | Jan 2014 | A1 |
20140016493 | Johnsson et al. | Jan 2014 | A1 |
20140019684 | Wei et al. | Jan 2014 | A1 |
20140025770 | Warfield | Jan 2014 | A1 |
20140029441 | Nydell | Jan 2014 | A1 |
20140029442 | Wallman | Jan 2014 | A1 |
20140039683 | Zimmermann et al. | Feb 2014 | A1 |
20140040473 | Ho et al. | Feb 2014 | A1 |
20140040883 | Tompkins | Feb 2014 | A1 |
20140047201 | Mehta | Feb 2014 | A1 |
20140053264 | Dubrovsky et al. | Feb 2014 | A1 |
20140059187 | Rosset et al. | Feb 2014 | A1 |
20140059266 | Ben-Michael et al. | Feb 2014 | A1 |
20140086253 | Yong | Mar 2014 | A1 |
20140089273 | Borshack | Mar 2014 | A1 |
20140095556 | Lee et al. | Apr 2014 | A1 |
20140096249 | Dupont et al. | Apr 2014 | A1 |
20140105009 | Vos et al. | Apr 2014 | A1 |
20140108474 | David | Apr 2014 | A1 |
20140109071 | Ding et al. | Apr 2014 | A1 |
20140112122 | Kapadia et al. | Apr 2014 | A1 |
20140123207 | Agarwal et al. | May 2014 | A1 |
20140156557 | Zeng et al. | Jun 2014 | A1 |
20140164666 | Yand | Jun 2014 | A1 |
20140164866 | Bolotov et al. | Jun 2014 | A1 |
20140172371 | Zhu et al. | Jun 2014 | A1 |
20140173060 | Jubran et al. | Jun 2014 | A1 |
20140173195 | Rosset et al. | Jun 2014 | A1 |
20140173579 | McDonald et al. | Jun 2014 | A1 |
20140189278 | Peng | Jul 2014 | A1 |
20140198794 | Mehta et al. | Jul 2014 | A1 |
20140211661 | Gorkemli et al. | Jul 2014 | A1 |
20140215265 | Mohanta et al. | Jul 2014 | A1 |
20140215590 | Brand | Jul 2014 | A1 |
20140219086 | Cantu′ et al. | Aug 2014 | A1 |
20140222953 | Karve et al. | Aug 2014 | A1 |
20140229790 | Goss et al. | Aug 2014 | A1 |
20140244585 | Sivasubramanian | Aug 2014 | A1 |
20140244897 | Goss et al. | Aug 2014 | A1 |
20140245435 | Belenky | Aug 2014 | A1 |
20140269390 | Ciodaru et al. | Sep 2014 | A1 |
20140281700 | Nagesharao et al. | Sep 2014 | A1 |
20140297941 | Rajani | Oct 2014 | A1 |
20140307578 | DeSanti | Oct 2014 | A1 |
20140317206 | Lomelino et al. | Oct 2014 | A1 |
20140324862 | Bingham et al. | Oct 2014 | A1 |
20140325208 | Resch et al. | Oct 2014 | A1 |
20140331276 | Frascadore et al. | Nov 2014 | A1 |
20140348166 | Yang et al. | Nov 2014 | A1 |
20140355450 | Bhikkaji et al. | Dec 2014 | A1 |
20140366155 | Chang et al. | Dec 2014 | A1 |
20140376550 | Khan et al. | Dec 2014 | A1 |
20150003450 | Salam et al. | Jan 2015 | A1 |
20150003458 | Li et al. | Jan 2015 | A1 |
20150003463 | Li et al. | Jan 2015 | A1 |
20150010001 | Duda et al. | Jan 2015 | A1 |
20150016461 | Qiang | Jan 2015 | A1 |
20150030024 | Venkataswami et al. | Jan 2015 | A1 |
20150046123 | Kato | Feb 2015 | A1 |
20150063353 | Kapadia et al. | Mar 2015 | A1 |
20150067001 | Koltsidas | Mar 2015 | A1 |
20150082432 | Eaton et al. | Mar 2015 | A1 |
20150092824 | Wicker, Jr. et al. | Apr 2015 | A1 |
20150120907 | Niestemski et al. | Apr 2015 | A1 |
20150121131 | Kiselev et al. | Apr 2015 | A1 |
20150127979 | Doppalapudi | May 2015 | A1 |
20150142840 | Baldwin et al. | May 2015 | A1 |
20150169313 | Katsura | Jun 2015 | A1 |
20150180672 | Kuwata | Jun 2015 | A1 |
20150207763 | Bertran Ortiz et al. | Jun 2015 | A1 |
20150205974 | Talley | Jul 2015 | A1 |
20150222444 | Sarkar | Aug 2015 | A1 |
20150229546 | Somaiya et al. | Aug 2015 | A1 |
20150248366 | Bergsten et al. | Sep 2015 | A1 |
20150248418 | Bhardwaj | Sep 2015 | A1 |
20150254003 | Lee et al. | Sep 2015 | A1 |
20150254088 | Chou et al. | Sep 2015 | A1 |
20150261446 | Lee | Sep 2015 | A1 |
20150263993 | Kuch et al. | Sep 2015 | A1 |
20150269048 | Marr et al. | Sep 2015 | A1 |
20150277804 | Arnold et al. | Oct 2015 | A1 |
20150281067 | Wu | Oct 2015 | A1 |
20150303949 | Jafarkhani et al. | Oct 2015 | A1 |
20150341237 | Cuni et al. | Nov 2015 | A1 |
20150341239 | Bertran Ortiz et al. | Nov 2015 | A1 |
20150358136 | Medard | Dec 2015 | A1 |
20150379150 | Duda | Dec 2015 | A1 |
20160004611 | Lakshman et al. | Jan 2016 | A1 |
20160011936 | Luby | Jan 2016 | A1 |
20160011942 | Golbourn et al. | Jan 2016 | A1 |
20160054922 | Awasthi et al. | Feb 2016 | A1 |
20160062820 | Jones et al. | Mar 2016 | A1 |
20160070652 | Sundararaman et al. | Mar 2016 | A1 |
20160087885 | Tripathi et al. | Mar 2016 | A1 |
20160088083 | Bharadwaj et al. | Mar 2016 | A1 |
20160119159 | Zhao et al. | Apr 2016 | A1 |
20160119421 | Semke et al. | Apr 2016 | A1 |
20160139820 | Fluman et al. | May 2016 | A1 |
20160149639 | Pham et al. | May 2016 | A1 |
20160205189 | Mopur et al. | Jul 2016 | A1 |
20160210161 | Rosset et al. | Jul 2016 | A1 |
20160231928 | Lewis et al. | Aug 2016 | A1 |
20160274926 | Narasimhamurthy et al. | Sep 2016 | A1 |
20160285760 | Dong | Sep 2016 | A1 |
20160292359 | Tellis et al. | Oct 2016 | A1 |
20160294983 | Kliteynik et al. | Oct 2016 | A1 |
20160366094 | Mason et al. | Dec 2016 | A1 |
20160378624 | Jenkins, Jr. et al. | Dec 2016 | A1 |
20160380694 | Guduru | Dec 2016 | A1 |
20170010874 | Rosset | Jan 2017 | A1 |
20170010930 | Dutta et al. | Jan 2017 | A1 |
20170019475 | Metz et al. | Jan 2017 | A1 |
20170068630 | Iskandar et al. | Mar 2017 | A1 |
20170131934 | Kaczmarczyk | May 2017 | A1 |
20170168970 | Sajeepa et al. | Jun 2017 | A1 |
20170177860 | Suarez et al. | Jun 2017 | A1 |
20170212858 | Chu et al. | Jul 2017 | A1 |
20170273019 | Park et al. | Sep 2017 | A1 |
20170277655 | Das et al. | Sep 2017 | A1 |
20170337097 | Sipos et al. | Nov 2017 | A1 |
20170340113 | Charest et al. | Nov 2017 | A1 |
20170371558 | George et al. | Dec 2017 | A1 |
20180097707 | Wright et al. | Apr 2018 | A1 |
Number | Date | Country |
---|---|---|
2228719 | Sep 2010 | EP |
2439637 | Apr 2012 | EP |
2680155 | Jan 2014 | EP |
2350028 | May 2001 | GB |
2000-242434 | Sep 2000 | JP |
1566104 | Jan 2017 | TW |
W0 2004077214 | Sep 2004 | WO |
WO 2016003408 | Jan 2016 | WO |
WO 2016003489 | Jan 2016 | WO |
Entry |
---|
Sage A. Weil, “Ceph: Reliable, Scalable, and High-Performance Distributed Storage,” University of California, Santa Cruz, Dec. 2007. |
Färber, et al., “An In-Memory Database System for Multi-Tenant Applications,” Proceedings of 14th Business, Technology and Web (BTW) Conference on “Database Systems for Business, Technology, and Web,” Feb. 28 to Mar. 4, 2011, University of Kaiserslautern, Germany, 17 pages; http://cs.emis.de/LNI/Proceedings/Proceedings180/650.pdf. |
Sage A. Weil, “Ceph: Reliable, Scalable, and High-Performance Distributed Storage,” University of California, Santa Cruz, Dec. 2007, 239 pages, on the Cisco Catalyst 4500 Classic Supervisor Engines, http://ceph.com/papers/weil-thesis.pdf. |
Guo, et al., “IBM Resarch Report: Data Integration and Composite Business Services, Part 3, Building a Multi-Tenant Data Tier with with [sic] Access Control and Security,” RC24426 (C0711-037) Nov. 19, 2007, Computer Science, 20 pages. |
Shue, et al., “Performance Isolation and Fairness for Multi-Tenant Cloud Storage,” USENIX Association, 10th USENIX Symposium on Operating Systems Design Implementation (OSDI '12), 14 pages; https://www.usenix.org/system/files/conference/osdi12/osdi12-final-215.pdf. |
Author Unknown, “5 Benefits of a Storage Gateway in the Cloud,” Blog, TwinStrata, Inc., posted Jul. 10, 2012, 4 pages, https://web.archive.org/web/20120725092619/http://blog.twinstrata.com/2012/07/10/5-benefits-of a storage-gateway-in-the-cloud. |
Author Unknown, “Configuration Interface for IBM System Storage DS5000, IBM DS4000, and IBM DS3000 Systems,” IBM SAN Volume Controller Version 7.1, IBM® System Storage® SAN Volume Controller Information Center, Jun. 16, 2013, 3 pages. |
Author Unknown, “Coraid EtherCloud, Software-Defined Storage with Scale-Out Infrastructure,” Solution Brief, 2013, 2 pages, Coraid, Redwood City, California, U.S.A. |
Author Unknown, “Coraid Virtual DAS (VDAS) Technology: Eliminate Tradeoffs between DAS and Networked Storage,” Coraid Technology Brief, © 2013 Cora id, Inc., Published on or about Mar. 20, 2013, 2 pages. |
Author Unknown, “Creating Performance-based SAN SLAs Using Finisar's NetWisdom” May 2006, 7 pages, Finisar Corporation, Sunnyvale, California, U.S.A. |
Author Unknown, “Data Center, Metro Cloud Connectivity: Integrated Metro SAN Connectivity in 16 Gbps Switches,” Brocade Communication Systems, Inc., Apr. 2011, 14 pages. |
Author Unknown, “Data Center, San Fabric Administration Best Practices Guide, Support Perspective,” Brocade Communication Systems, Inc., May 2013, 21 pages. |
Author Unknown, “delphi—Save a CRC value in a file, without altering the actual CRC Checksum?” Stack Overflow, stackoverflow.com, Dec. 23, 2011, XP055130879, 3 pages http://stackoverflow.com/questions/8608219/save-a-crc-value-in-a-file-without-altering-the-actual-crc-checksum. |
Author Unknown, “EMC UNISPHERE: Innovative Approach to Managing Low-End and Midrange Storage; Redefining Simplicity in the Entry-Level and Midrange Storage Markets,” Data Sheet, EMC Corporation; published on or about Jan. 4, 2013 [Retrieved and printed Sep. 12, 2013], 6 pages http://www.emc.com/storage/vnx/unisphere.htm. |
Author Unknown, “HP XP Array Manager Software—Overview & Features,” Storage Device Management Software; Hewlett-Packard Development Company, 3 pages; © 2013 Hewlett-Packard Development Company, L.P. |
Author Unknown, “Joint Cisco and VMWare Solution for Optimizing Virtual Desktop Delivery: Data Center 3.0: Solutions to Accelerate Data Center Virtualization,” Cisco Systems, Inc. And VMware, Inc., Sep. 2008, 10 pages. |
Author Unknown, “Network Transformation with Software-Defined Networking and Ethernet Fabrics,” Positioning Paper, 2012, 6 pages, Brocade Communications Systems. |
Author Unknown, “Recreating Real Application Traffic in Junosphere Lab,” Solution Brief, Juniper Networks, Dec. 2011, 3 pages. |
Author Unknown, “Shunra for HP Softwarer,” Enabiling Confidence in Application Performance Before Deployment, 2010, 2 pages. |
Author Unknown, “Software Defined Networking: The New Norm for Networks,” White Paper, Open Networking Foundation, Apr. 13, 2012, 12 pages. |
Author Unknown, “Software Defined Storage Networks an Introduction,” White Paper, Doc # 01-000030-001 Rev. A, Dec. 12, 2012, 8 pages; Jeda Networks, Newport Beach, California, U.S.A. |
Author Unknown, “Standard RAID Levels,” Wikipedia, the Free Encyclopedia, last updated Jul. 18, 2014, 7 pages; http://en.wikipedia.org/wiki/Standard_RAID_levels. |
Author Unknown, “Storage Infrastructure for the Cloud,” Solution Brief, © 2012, 3 pages; coraid, Redwood City, California, U.S.A. |
Author Unknown, “Storage Area Network—NPIV: Emulex Virtual HBA and Brocade, Proven Interoperability and Proven Solution,” Technical Brief, Apr. 2008, 4 pages, Emulex and Brocade Communications Systems. |
Author Unknown, “The Fundamentals of Software-Defined Storage, Simplicity at Scale for Cloud-Architectures” Solution Brief, 2013, 3 pages; Coraid, Redwood City, California, U.S.A. |
Author Unknown, “VirtualWisdom® SAN Performance Probe Family Models: Probe FC8, HD, and HD48,” Virtual Instruments Data Sheet, Apr. 2014 Virtual Instruments. All Rights Reserved; 4 pages. |
Author Unknown, “Xgig Analyzer: Quick Start Feature Guide 4.0,” Feb. 2008, 24 pages, Finisar Corporation, Sunnyvale, California, U.S.A. |
Author Unknown, “Sun Storage Common Array Manager Installation and Setup Guide,” Software Installation and Setup Guide Version 6.7.x 821-1362-10, Appendix D: Configuring In-Band Management, Sun Oracle; retrieved and printed Sep. 12, 2013, 15 pages. |
Author Unknown, “VBLOCK Solution for SAP: Simplified Provisioning for Operation Efficiency,” VCE White Paper, VCE—The Virtual Computing Environment Company, Aug. 2011, 11 pages. |
Berman, Stuart, et al., “Start-Up Jeda Networks in Software Defined Storage Network Technology,” Press Release, Feb. 25, 2013, 2 pages, http://www.storagenewsletter.com/news/startups/jeda-networks. |
Borovick, Lucinda, et al., “White Paper, Architecting the Network for the Cloud,” IDC Analyze the Future, Jan. 2011, pp. 1-8. |
Chakrabarti, Kaushik, et al., “Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases,” ACM Transactions on Database Systems, vol. 27, No. 2, Jun. 2009, pp. 188-228. |
Chandola, Varun, et al., “A Gaussian Process Based Online Change Detection Algorithm for Monitoring Periodic Time Series,” Proceedings of the Eleventh SIAM International Conference on Data Mining, SDM 2011, Apr. 28-30, 2011, 12 pages. |
Cisco Systems, Inc. “N-Port Virtualization in the Data Center,” Cisco White Paper, Cisco Systems, Inc., Mar. 2008, 7 pages. |
Cisco Systems, Inc., “Best Practices in Deploying Cisco Nexus 1000V Series Switches on Cisco UCS B and C Series Cisco UCS Manager Servers,” White Paper, Cisco Systems, Inc., Apr. 2011, 36 pages. |
Cisco Systems, Inc., “Cisco Prime Data Center Network Manager 6.1,” At-A-Glance, © 2012, 3 pages. |
Cisco Systems, Inc., “Cisco Prime Data Center Network Manager,” Release 6.1 Data Sheet, © 2012, 10 pages. |
Cisco Systems, Inc., “Cisco Unified Network Services: Overcome Obstacles to Cloud-Ready Deployments,” White Paper, Cisco Systems, Inc., Jan. 2011, 6 pages. |
Clarke, Alan, et al., “Open Data Center Alliance Usage: Virtual Machine (VM) Interoperability in a Hybrid Cloud Environment Rev. 1.2,” Open Data Center Alliance, Inc., 2013, pp. 1-18. |
Cummings, Roger, et al., Fibre Channel—Fabric Generic Requirements (FC-FG), Dec. 4, 1996, 33 pages, American National Standards Institute, Inc., New York, New York, U.S.A. |
Hatzieleftheriou, Andromachi, et al., “Host-side Filesystem Journaling for Durable Shared Storage,” 13th USENIX Conference on File and Storage Technologies (FAST '15), Feb. 16-19, 2015, 9 pages; https://www.usenix.org/system/files/conference/fast15/fast15-paper-hatzieleftheriou.pdf. |
Hedayat, K., et al., “A Two-Way Active Measurement Protocol (TWAMP),” Network Working Group, RFC 5357, Oct. 2008, 26 pages. |
Horn, C., et al., “Online anomaly detection with expert system feedback in social networks,” 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 22-27, 2011, 2 pages, Prague; [Abstract only]. |
Hosterman, Cody, et al., “Using EMC Symmetrix Storage inVMware vSph ere Environments,” Version 8.0, EMC2Techbooks, EMC Corporation; published on or about Jul. 8, 2008, 314 pages; [Retrieved and printed Sep. 12, 2013]. |
Hu, Yuchong, et al., “Cooperative Recovery of Distributed Storage Systems from Multiple Losses with Network Coding,” University of Science & Technology of China, Feb. 2010, 9 pages. |
Keogh, Eamonn, et al., “Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases,” KAIS Long Paper submitted May 16, 2000; 19 pages. |
Kolyshkin, Kirill, “Virtualization in Linux,” Sep. 1, 2006, pp. 1-5. |
Kovar, Joseph F., “Startup Jeda Networks Takes SDN Approach to Storage Networks,” CRN Press Release, Feb. 22, 2013, 1 page, http://www.crn.com/240149244/printablearticle.htm. |
Lampson, Butler, W., et al., “Crash Recovery in a Distributed Data Storage System,” Jun. 1, 1979, 28 pages. |
Lewis, Michael E., et al., “Design of an Advanced Development Model Optical Disk-Based Redundant Array of Independent Disks (RAID) High Speed Mass Storage Subsystem,” Final Technical Report, Oct. 1997, pp. 1-211. |
Lin, Jessica, “Finding Motifs in Time Series,” SIGKDD'02 Jul. 23-26, 2002, 11 pages, Edmonton, Alberta, Canada. |
Linthicum, David, “VM Import could be a game changer for hybrid clouds”, InfoWorld, Dec. 23, 2010, 4 pages. |
Long, Abraham Jr., “Modeling the Reliability of RAID Sets,” Dell Power Solutions, May 2008, 4 pages. |
Ma, Ao, et al., “RAIDShield: Characterizing, Monitoring, and Proactively Protecting Against Disk Failures,” FAST '15, 13th USENIX Conference on File and Storage Technologies, Feb. 16-19, 2015, 17 pages, Santa Clara, California, U.S.A. |
Mahalingam, M., et al., “Virtual eXtensible Local Area Network (VXLAN): A Framework for Overlaying Virtualized Layer 2 Networks over Layer 3 Networks,” Independent Submission, RFC 7348, Aug. 2014, 22 pages; http://www.hjp.at/doc/rfc/rfc7348.html. |
McQuerry, Steve, “Cisco UCS M-Series Modular Servers for Cloud-Scale Workloads,” White Paper, Cisco Systems, Inc., Sep. 2014, 11 pages. |
Monia, Charles, et al., IFCP—A Protocol for Internet Fibre Channel Networking, draft-monia-ips-ifcp-00.txt, Dec. 12, 2000, 6 pages. |
Mueen, Abdullah, et al., “Online Discovery and Maintenance of Time Series Motifs,” KDD'10 The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Jul. 25-28, 2010, 10 pages, Washington, DC, U.S.A. |
Muglia, Bob, “Decoding SDN,” Jan. 14, 2013, Juniper Networks, pp. 1-7, http://forums.juniper.net/t5/The-New-Network/Decoding-SDN/ba-p/174651. |
Murray, Joseph F., et al., “Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application,” Journal of Machine Learning Research 6 (2005), pp. 783-816; May 2005, 34 pages. |
Nelson, Mark, “File Verification Using CRC,” Dr. Dobb's Journal, May 1, 1992, pp. 1-18, XP055130883. |
Pace, Alberto, “Technologies for Large Data Management in Scientific Computing,” International Journal of Modern Physics C., vol. 25, No. 2, Feb. 2014, 72 pages. |
Pinheiro, Eduardo, et al., “Failure Trends in a Large Disk Drive Population,” FAST '07, 5th USENIX Conference on File and Storage Technologies, Feb. 13-16, 2007, 13 pages, San Jose, California, U.S.A. |
Raginsky, Maxim, et al., “Sequential Anomaly Detection in the Presence of Noise and Limited Feedback,” arXiv:0911.2904v4 [cs.LG] Mar. 13, 2012, 19 pages. |
Saidi, Ali G., et al., “Performance Validation of Network-Intensive Workloads on a Full-System Simulator,” Interaction between Operating System and Computer Architecture Workshop, (IOSCA 2005), Austin, Texas, Oct. 2005, 10 pages. |
Sajassi, A., et al., “BGP MPLS Based Ethernet VPN,” Network Working Group, Oct. 18, 2014, 52 pages. |
Sajassi, Ali, et al., “A Network Virtualization Overlay Solution using EVPN,” L2VPN Workgroup, Nov. 10, 2014, 24 pages; http://tools.ietf.org/pdf/draft-ietf-bess-evpn-overlay-00.pdf. |
Sajassi, Ali, et al., “Integrated Routing and Bridging in EVPN,” L2VPN Workgroup, Nov. 11, 2014, 26 pages; http://tools.ietf.org/pdf/draft-ietf-bess-evpn-inter-subnet-forwarding-00.pdf. |
Schroeder, Bianca, et al., “Disk failures in the real world: What does an MTTF of 1,000,000 hours mean to you?” FAST '07: 5th USENIX Conference on File and Storage Technologies, Feb. 13-16, 2007, 16 pages, San Jose, California, U.S.A. |
Staimer, Marc, “Inside Cisco Systems' Unified Computing System,” Dragon Slayer Consulting, Jul. 2009, 5 pages. |
Swami, Vijay, “Simplifying SAN Management for VMWare Boot from SAN, Utilizing Cisco UCS and Palo,” posted May 31, 2011, 6 pages. |
Tate, Jon, et al., “Introduction to Storage Area Networks and System Networking,” Dec. 2017, 302 pages, ibm.com/redbooks. |
Vuppala, Vibhavasu, et al., “Layer-3 Switching Using Virtual Network Ports,” Computer Communications and Networks, 1999, Proceedings, Eight International Conference in Boston, MA, USA, Oct. 11-13, 1999, Piscataway, NJ, USA, IEEE, ISBN: 0-7803-5794-9, pp. 642-648. |
Wang, Feng, et al. “OBFS: A File System for Object-Based Storage Devices,” Storage System Research Center, MSST. vol. 4., Apr. 2004, 18 pages. |
Weil, Sage A., et al. “CRUSH: Controlled, Scalable, Decentralized Placement of Replicated Data.” Proceedings of the 2006 ACM/IEEE conference on Supercomputing. ACM, Nov. 11, 2006, 12 pages. |
Weil, Sage A., et al. “Ceph: A Scalable, High-performance Distributed File System,” Proceedings of the 7th symposium on Operating systems design and implementation. USENIX Association, Nov. 6, 2006, 14 pages. |
Wu, Joel, et al., “The Design, and Implementation of AQuA: An Adaptive Quality of Service Aware Object-Based Storage Device,” Department of Computer Science, MSST, May 17, 2006, 25 pages; http://storageconference.us/2006/Presentations/30Wu.pdf. |
Xue, Chendi, et al. “A Standard framework for Ceph performance profiling with latency breakdown,” CEPH, Jun. 30, 2015, 3 pages. |
Zhou, Zihan, et al., “Stable Principal Component Pursuit,” arXiv:1001.2363v1 [cs.IT], Jan. 14, 2010, 5 pages. |
Zhu, Yunfeng, et al., “A Cost-based Heterogeneous Recovery Scheme for Distributed Storage Systems with RAID-6 Codes,” University of Science & Technology of China, 2012, 12 pages. |
Stamey, John, et al., “Client-Side Dynamic Metadata in Web 2.0,” SIGDOC '07, Oct. 22-24, 2007, pp. 155-161. |
Aweya, James, et al., “Multi-level active queue management with dynamic thresholds,” Elsevier, Computer Communications 25 (2002) pp. 756-771. |
Petersen, Chris, “Introducing Lightning: A flexible NVMe JBOF,” Mar. 9, 2016, 6 pages. |
Number | Date | Country | |
---|---|---|---|
20160334998 A1 | Nov 2016 | US |