Patent Application
20210034484
This application relates to managing asynchronous replication.
Many information processing systems are configured to replicate data from a storage system at one site to a storage system at another site. In some cases, such arrangements are utilized to support disaster recovery functionality within the information processing system. For example, an enterprise may replicate data from a production data center to a disaster recovery data center. In the event of a disaster at the production site, applications can be started at the disaster recovery site using the data that has been replicated to that site so that the enterprise can continue its business.
Data replication in these and other contexts can be implemented using asynchronous replication at certain times and synchronous replication at other times. For example, asynchronous replication may be configured to periodically transfer data in multiple cycles from a source site to a target site, while synchronous replication may be configured to mirror host writes from the source site to the target site as the writes are made at the source site. Source site and target site storage systems can therefore each be configured to support both asynchronous and synchronous replication modes.
Described below is a technique for use in managing asynchronous replication, which technique may be used to provide, receiving a multi-page replication request in conjunction with the replication process, where a first storage system comprises a plurality of storage devices and a second storage system comprises a plurality of storage devices, where the first storage system is configured to participate in a replication process with the second storage system, determining at least one replication condition meets a threshold, and in response, optimizing the multi-page replication request to optimize the replication process.
As described herein, in at least one embodiment of the current technique, the method receives a multi-page replication request in conjunction with the replication process, where a first storage system comprises a plurality of storage devices and a second storage system comprises a plurality of storage devices, and where the first storage system is configured to participate in a replication process with the second storage system. The method determines at least one replication condition meets a threshold, and in response, optimizes the multi-page replication request to optimize the replication process.
Typically, a replication process between two storage devices may execute with two types of replication modes. A synchronous replication mode, in some embodiments, is configured to mirror data writes between a first storage system, and a second storage system. For example, when a host device writes data to the first storage system, the first storage system responds to the host device with an acknowledgement of successful storage in the first storage system only after the first storage system sends the data to (i.e., replicates the data to) the second storage system and receives an acknowledgement of successful storage back from the second storage system. Therefore, the synchronous replication mode is sensitive to the latency of the replication process.
The asynchronous replication mode, in some embodiments, implements cycle-based asynchronous replication to periodically transfer data in multiple cycles from the first storage system to the second storage system. The data replicated from the first storage system to the second storage system may include all of the data stored in the first storage system, or only certain designated subsets of the data stored in the first storage system. Different replication processes of different types may be implemented for different parts of the stored data. The asynchronous replication mode is sensitive to bandwidth, for example, how many megabytes (MB) per second can be transferred from the first storage system to the second storage system. In the asynchronous replication mode, the host receives acknowledgment and is not waiting for the data transfer, and therefore, latency is not as important to the asynchronous replication mode as is the available bandwidth.
Conventional approaches to data replication between two storage devices can be problematic under certain conditions. For example, a replication engine or other arrangement of replication control logic that manages replication requests may be negatively impacted by the availability and or expense of bandwidth. Conventional technologies do not provide a solution for optimizing a replication process when bandwidth between the two storage devices is limited and/or when the bandwidth is expensive. Conventional technologies do not minimize replication bandwidth. Conventional technologies do not provide a solution that optimizes the replication process when similar data is stored in nearby pages on one of the storage devices. Conventional technologies do not optimize data compression when Central Processing Unit (CPU) bandwidth is plentiful. Conventional technologies handle pages of a replication request individually. Conventional technologies miss compression opportunities that may exist in multi-page replication request. Conventional technologies do not optimize compression opportunities in multi-page replication requests.
By contrast, in at least some implementations in accordance with the current technique as described herein, a method receives a multi-page replication request in conjunction with the replication process, where a first storage system comprises a plurality of storage devices and a second storage system comprises a plurality of storage devices, and where the first storage system is configured to participate in a replication process with the second storage system. The method determines at least one replication condition meets a threshold, and in response, optimizes the multi-page replication request to optimize the replication process.
Thus, in at least one embodiment of the current technique, by optimizing data compression, the method reduces the bandwidth needed to complete the replication process, and in doing so, optimizes the replication process.
Thus, a goal of the current technique is to provide a method and a system for managing asynchronous replication. Another goal is to optimize the replication process, especially when bandwidth is limited and/or expensive. Another goal is to minimize replication bandwidth. Another goal is to optimize data compression when CPU bandwidth is plentiful. Yet another goal is to optimize compression opportunities in multi-page segments.
In at least some implementations in accordance with the current technique described herein, managing asynchronous replication can provide one or more of the following advantages: optimizing the replication process when bandwidth is limited and/or expensive, minimizing replication bandwidth, optimizing the replication process when similar data is stored in nearby pages on one of the storage devices, optimizing data compression when CPU bandwidth is plentiful, and identifying and optimizing compression opportunities in multi-page segments, etc.
In contrast to conventional technologies, in at least some implementations in accordance with the current technique as described herein, a method receives a multi-page replication request in conjunction with the replication process, where a first storage system comprises a plurality of storage devices and a second storage system comprises a plurality of storage devices, and where the first storage system is configured to participate in a replication process with the second storage system. The method determines at least one replication condition meets a threshold, and in response, optimizes the multi-page replication request to optimize the replication process.
In an example embodiment of the current technique, the replication condition includes Central Processing Unit (CPU) usage.
In an example embodiment of the current technique, the replication condition includes replication bandwidth.
In an example embodiment of the current technique, the method groups at least two pages of the multi-page replication request into a contiguous segment and performs compression on the segment.
In an example embodiment of the current technique, the method groups at least two pages of the multi-page replication request according to addresses associated with at least two pages where the multi-page replication request is comprised of a plurality of addresses, and each of the plurality of addresses has a respective page pointer.
In an example embodiment of the current technique, the addresses associated with the grouped pages are adjacent addresses.
In an example embodiment of the current technique, the method groups at least two pages of the multi-page replication request according to an optimum page size.
In an example embodiment of the current technique, the optimum page size correlates to an compression ratio.
In an example embodiment of the current technique, the method groups at least two pages of the multi-page replication request according to a compression ratio associated with each of the grouped pages of the multi-page replication request.
In an example embodiment of the current technique, grouping at least two pages of the multi-page replication request results in an improved compression ratio over a respective compression ratio associated with each of the pages of the multi-page replication request.
In an example embodiment of the current technique, the method receives, at the second storage system, the compressed segment, decompresses the compressed segment into a plurality of decompressed pages, and compresses at least one of the plurality of decompressed pages.
In an example embodiment of the current technique, the method detects that at least one page of the multi-page replication request is compressed and decompresses the page.
In an example embodiment of the current technique, the method groups at least two pages of a plurality of multi-page replication requests into a contiguous segment and performs compression on the segment.
In an example embodiment of the current technique, the method optimizes the multi-page replication request to minimize bandwidth required to complete the replication process.
Illustrative embodiments will be described herein with reference to exemplary information processing systems and associated computers, servers, storage devices and other processing devices. It is to be appreciated, however, that these and other embodiments are not restricted to the particular illustrative system and device configurations shown. Accordingly, the term “information processing system” as used herein is intended to be broadly construed, so as to encompass, for example, processing systems comprising cloud computing and storage systems, as well as other types of processing systems comprising various combinations of physical and virtual processing resources. An information processing system may therefore comprise, for example, at least one data center or other cloud-based system that includes one or more clouds hosting multiple tenants that share cloud resources. Numerous other types of enterprise computing and storage systems are also encompassed by the term “information processing system” as that term is broadly used herein.
The compute nodes 102 illustratively comprise respective processing devices of one or more processing platforms. For example, the compute nodes 102 can comprise respective virtual machines (VMs) each having a processor and a memory, although numerous other configurations are possible.
The compute nodes 102 can additionally or alternatively be part of cloud infrastructure such as an Amazon Web Services (AWS) system. Other examples of cloud-based systems that can be used to provide compute nodes 102 and possibly other portions of system 100 include Google Cloud Platform (GCP) and Microsoft Azure.
The compute nodes 102 in some embodiments illustratively provide compute services such as execution of one or more applications on behalf of each of one or more users associated with respective ones of the compute nodes 102.
The term “user” herein is intended to be broadly construed so as to encompass numerous arrangements of human, hardware, software or firmware entities, as well as combinations of such entities. Compute and/or storage services may be provided for users under a platform-as-a-service (PaaS) model, although it is to be appreciated that numerous other cloud infrastructure arrangements could be used. Also, illustrative embodiments can be implemented outside of the cloud infrastructure context, as in the case of a stand-alone enterprise-based computing and storage system.
The network 104 is assumed to comprise a portion of a global computer network such as the Internet, although other types of networks can be part of the network 104, including a wide area network (WAN), a local area network (LAN), a satellite network, a telephone or cable network, a cellular network, a wireless network such as a WiFi or WiMAX network, or various portions or combinations of these and other types of networks. The network 104 in some embodiments therefore comprises combinations of multiple different types of networks each comprising processing devices configured to communicate using Internet Protocol (IP) or other communication protocols.
As a more particular example, some embodiments may utilize one or more high-speed local networks in which associated processing devices communicate with one another utilizing Peripheral Component Interconnect express (PCIe) cards of those devices, and networking protocols such as InfiniBand, Gigabit Ethernet or Fibre Channel. Numerous alternative networking arrangements are possible in a given embodiment, as will be appreciated by those skilled in the art.
The content addressable storage system 105 is accessible to the compute nodes 102 of the computer system 101 over the network 104. The content addressable storage system 105 comprises a plurality of storage devices 106 and an associated storage controller 108. The storage devices 106 are configured to store metadata pages 110 and user data pages 112, and may also store additional information not explicitly shown such as checkpoints and write journals. The metadata pages 110 and the user data pages 112 are illustratively stored in respective designated metadata and user data areas of the storage devices 106. Accordingly, metadata pages 110 and user data pages 112 may be viewed as corresponding to respective designated metadata and user data areas of the storage devices 106.
A given “page” as the term is broadly used herein should not be viewed as being limited to any particular range of fixed sizes. In some embodiments, a page size of 8 kilobytes (KB) is used, but this is by way of example only and can be varied in other embodiments. For example, page sizes of 4 KB or other values can be used. Accordingly, illustrative embodiments can utilize any of a wide variety of alternative paging arrangements for organizing the metadata pages 110 and the user data pages 112.
The user data pages 112 are part of a plurality of logical units (LUNs) configured to store files, blocks, objects or other arrangements of data on behalf of users associated with compute nodes 102. Each such LUN may comprise particular ones of the above-noted pages of the user data area. The user data stored in the user data pages 112 can include any type of user data that may be utilized in the system 100. The term “user data” herein is therefore also intended to be broadly construed.
It is assumed in the present embodiment that the storage devices 106 comprise solid state drives (SSDs). Such SSDs are implemented using non-volatile memory (NVM) devices such as flash memory. Other types of NVM devices that can be used to implement at least a portion of the storage devices 106 include non-volatile random access memory (NVRAM), phase-change RAM (PC-RAM) and magnetic RAM (MRAM). Various combinations of multiple different types of NVM devices may also be used.
However, it is to be appreciated that other types of storage devices can be used in other embodiments. For example, a given storage system as the term is broadly used herein can include a combination of different types of storage devices, as in the case of a multi-tier storage system comprising a flash-based fast tier and a disk-based capacity tier. In such an embodiment, each of the fast tier and the capacity tier of the multi-tier storage system comprises a plurality of storage devices with different types of storage devices being used in different ones of the storage tiers. For example, the fast tier may comprise flash drives while the capacity tier comprises hard disk drives. The particular storage devices used in a given storage tier may be varied in other embodiments, and multiple distinct storage device types may be used within a single storage tier. The term “storage device” as used herein is intended to be broadly construed, so as to encompass, for example, flash drives, solid state drives, hard disk drives, hybrid drives or other types of storage devices.
In some embodiments, the content addressable storage system 105 illustratively comprises a scale-out all-flash storage array such as an XtremIO™ storage array from Dell EMC of Hopkinton, Mass. Other types of storage arrays, including by way of example VNX® and Symmetrix VMAX® storage arrays also from Dell EMC, can be used to implement storage systems in other embodiments.
The term “storage system” as used herein is therefore intended to be broadly construed, and should not be viewed as being limited to content addressable storage systems or flash-based storage systems. A given storage system as the term is broadly used herein can comprise, for example, network-attached storage (NAS), storage area networks (SANs), direct-attached storage (DAS) and distributed DAS, as well as combinations of these and other storage types, including software-defined storage.
Other particular types of storage products that can be used in implementing a given storage system in an illustrative embodiment include all-flash and hybrid flash storage arrays such as Unity™, software-defined storage products such as ScaleIO™ and ViPR®, cloud storage products such as Elastic Cloud Storage (ECS), object-based storage products such as Atmos®, and scale-out NAS clusters comprising Isilon® platform nodes and associated accelerators, all from Dell EMC. Combinations of multiple ones of these and other storage products can also be used in implementing a given storage system in an illustrative embodiment.
The content addressable storage system 105 in the embodiment of
The generation and storage of the hash metadata is assumed to be performed under the control of the storage controller 108. The hash metadata may be stored in the metadata area in a plurality of entries corresponding to respective buckets each comprising multiple cache lines, although other arrangements can be used.
Each of the metadata pages 110 characterizes a plurality of the user data pages 112. For example, as illustrated in
Each of the metadata pages 110 in the present embodiment is assumed to have a signature that is not content-based. For example, the metadata page signatures may be generated using hash functions or other signature generation algorithms that do not utilize content of the metadata pages as input to the signature generation algorithm. Also, each of the metadata pages is assumed to characterize a different set of the user data pages.
This is illustrated in
The content addressable storage system 105 in the
The storage controller 108 of the content addressable storage system 105 is implemented in a distributed manner so as to comprise a plurality of distributed storage controller components implemented on respective ones of the storage nodes 115 of the content addressable storage system 105. The storage controller 108 is therefore an example of what is more generally referred to herein as a “distributed storage controller.” In subsequent description herein, the storage controller 108 may be more particularly referred to as a distributed storage controller.
Each of the storage nodes 115 in this embodiment further comprises a set of processing modules configured to communicate over one or more networks with corresponding sets of processing modules on other ones of the storage nodes 115. The sets of processing modules of the storage nodes 115 collectively comprise at least a portion of the distributed storage controller 108 of the content addressable storage system 105.
The distributed storage controller 108 in the present embodiment is configured to implement functionality for managing asynchronous replication as used in a replication process carried out, for example, between the content addressable storage system 105 and another storage system. The term “replication process” as used herein is intended to be broadly construed, so as to encompass a single replication process that includes separate asynchronous and synchronous replication modes, as well as a replication process that includes multiple separate asynchronous and synchronous replication processes. In an arrangement of the latter type, the asynchronous and synchronous replication processes are also considered examples of what are more generally referred to herein as respective asynchronous and synchronous “replication modes.”
The modules of the distributed storage controller 108 in the present embodiment more particularly comprise different sets of processing modules implemented on each of the storage nodes 115. The set of processing modules of each of the storage nodes 115 comprises at least a control module 108C, a data module 108D and a routing module 108R. The distributed storage controller 108 further comprises one or more management (“MGMT”) modules 108M. For example, only a single one of the storage nodes 115 may include a management module 108M. It is also possible that management modules 108M may be implemented on each of at least a subset of the storage nodes 115.
Communication links are established between the various processing modules of the distributed storage controller 108 using well-known communication protocols such as Transmission Control Protocol (TCP) and Internet Protocol (IP). For example, respective sets of IP links used in replication data transfer could be associated with respective different ones of the routing modules 108R and each such set of IP links could include a different bandwidth configuration.
Ownership of a user data logical address space within the content addressable storage system 105 is illustratively distributed among the control modules 108C. The management module 108M is assumed to include a replication engine or other arrangement of replication control logic that engages corresponding replication control logic instances in all of the control modules 108C and routing modules 108R in order to implement a data replication process within the system 100, as will be described in more detail below in conjunction with
In some embodiments, the content addressable storage system 105 comprises an XtremIO™ storage array suitably modified to incorporate managing asynchronous replication techniques as disclosed herein. In arrangements of this type, the control modules 108C, data modules 108D and routing modules 108R of the distributed storage controller 108 illustratively comprise respective C-modules, D-modules and R-modules of the XtremIO™ storage array. The one or more management modules 108M of the distributed storage controller 108 in such arrangements illustratively comprise a system-wide management module (“SYM module”) of the XtremIO™ storage array, although other types and arrangements of system-wide management modules can be used in other embodiments.
Accordingly, managing asynchronous replication used in a replication process in some embodiments is implemented under the control of at least one system-wide management module of the distributed storage controller 108.
In the above-described XtremIO™ storage array example, each user data page typically has a size of 8 KB and its content-based signature is a 20-byte signature generated using an SHA1 hash function. Also, each page has a LUN identifier and an offset, and so is characterized by <lun_id, offset, signature>.
As mentioned previously, storage controller components in an XtremIO™ storage array illustratively include C-module and D-module components. For example, separate instances of such components can be associated with each of a plurality of storage nodes in a clustered storage system implementation.
The distributed storage controller in this example is configured to group consecutive pages into page groups, to arrange the page groups into slices, and to assign the slices to different ones of the C-modules.
The D-module allows a user to locate a given user data page based on its signature. Each metadata page also has a size of 8 KB and includes multiple instances of the <lun_id, offset, signature> for respective ones of a plurality of the user data pages. Such metadata pages are illustratively generated by the C-module but are accessed using the D-module based on a metadata page signature.
The metadata page signature in this embodiment is a 20-byte signature but is not based on the content of the metadata page. Instead, the metadata page signature is generated based on an 8-byte metadata page identifier that is a function of the LUN identifier and offset information of that metadata page.
If a user wants to read a user data page having a particular LUN identifier and offset, the corresponding metadata page identifier is first determined, then the metadata page signature is computed for the identified metadata page, and then the metadata page is read using the computed signature. In this embodiment, the metadata page signature is more particularly computed using a signature generation algorithm that generates the signature to include a hash of the 8-byte metadata page identifier, one or more ASCII codes for particular predetermined characters, as well as possible additional fields. The last bit of the metadata page signature may always be set to a particular logic value so as to distinguish it from the user data page signature in which the last bit may always be set to the opposite logic value.
The metadata page signature is used to retrieve the metadata page via the D-module. This metadata page will include the <lun_id, offset, signature> for the user data page if the user page exists. The signature of the user data page is then used to retrieve that user data page, also via the D-module.
Additional examples of content addressable storage functionality implemented in some embodiments by control modules 108C, data modules 108D, routing modules 108R and management module(s) 108M of distributed storage controller 108 can be found in U.S. Pat. No. 9,104,326, entitled “Scalable Block Data Storage Using Content Addressing,” which is incorporated by reference herein. Alternative arrangements of these and other storage node processing modules of a distributed storage controller in a content addressable storage system can be used in other embodiments.
The content addressable storage system 105 in the
Referring now to
The management module 108M of the distributed storage controller 108 in this embodiment more particularly comprises a system-wide management module or SYM module of the type mentioned previously. Although only a single SYM module is shown in this embodiment, other embodiments can include multiple instances of the SYM module possibly implemented on different ones of the storage nodes. It is therefore assumed that the distributed storage controller 108 comprises one or more management modules 108M.
A given instance of management module 108M comprises replication control logic 400 and associated management program code 402. The management module 108M communicates with control modules 108C-1 through 108C-x, also denoted as C-module 1 through C-module x.
The control modules 108C communicate with routing modules 108R-1 through 108R-y, also denoted as R-module 1 through R-module y. The variables x and y are arbitrary integers greater than one and may but need not be equal. In some embodiments, each of the storage nodes 115 of the content addressable storage system 105 comprises one of the control modules 108C and one of the routing modules 108R, as well as one or more additional modules including one of the data modules 108D.
The control modules 108C-1 through 108C-x in the
For example, a data transfer request for the given data page in the asynchronous replication mode is illustratively initiated by a given one of the control modules 108C directing a control-to-routing message to a given one of the routing modules 108R. The control-to-routing message is generated by the corresponding one of the message generators 404C, under the control of the corresponding instance of replication control logic 406C, operating in conjunction with replication control logic 400 of the management module 108M. The control-to-routing message comprises a logical page address of the given data page, including an identifier of a corresponding LUN and an offset of the given data page within the LUN. It also includes a current content-based signature for the data page, and may include additional information, such as various external identifying information for the corresponding storage volume.
The routing modules 108R-1 through 108R-y in the
The given one of the routing modules 108R that receives the above-noted data transfer request is configured to update the content-based signature of the given data page. This illustratively involves reading the given data page and recomputing the content-based signature of the given data page responsive to a determination that current content of the given data page is inconsistent with an existing content-based signature of the given data page. The given routing module 108R reads the given data page under the control of its corresponding instance of replication control logic 406R utilizing a read operation that automatically holds an address lock on the given data page for a duration of the read operation. The content-based signature is illustratively updated for the given data page by applying a secure hashing algorithm to content of the given data page.
The other control logic instances 406C and 406R in the other control and routing modules 108C and 108R are similarly configured to control the transmission of control-to-routing messages and associated routing-to-control messages in order to implement portions of a replication process as disclosed herein.
Portions of the replication process executed at the source site are collectively implemented by the instances of replication control logic 400, 406C and 406R of the respective storage node processing modules 108M, 108C and 108R of the first storage system. Similar instances of replication control logic in the second storage system are configured to perform the target site portions of the replication process.
The particular interconnection and signaling arrangements illustrated for processing modules 108C, 108R and 108M in
In some embodiments, the replication control logic of these processing modules comprises at least a portion of a replication engine of the storage controller 108.
It should also be understood that the particular arrangement of storage controller processing modules 108C, 108D, 108R and 108M as shown in the
Although illustratively shown as being implemented within the content addressable storage system 105, the storage controller 108 in other embodiments can be implemented at least in part within the computer system 101, in another system component, or as a stand-alone component coupled to the network 104.
The computer system 101 and content addressable storage system 105 in the
As a more particular example, the storage controller 108 can be implemented in the form of one or more LXCs running on one or more VMs. Other arrangements of one or more processing devices of a processing platform can be used to implement the storage controller 108. Other portions of the system 100 can similarly be implemented using one or more processing devices of at least one processing platform.
The computer system 101 and the content addressable storage system 105 may be implemented on respective distinct processing platforms, although numerous other arrangements are possible. For example, in some embodiments at least portions of the computer system 101 and the content addressable storage system 105 are implemented on the same processing platform. The content addressable storage system 105 can therefore be implemented at least in part within at least one processing platform that implements at least a subset of the compute nodes 102.
The term “processing platform” as used herein is intended to be broadly construed so as to encompass, by way of illustration and without limitation, multiple sets of processing devices and associated storage systems that are configured to communicate over one or more networks. For example, distributed implementations of the system 100 are possible, in which certain components of the system reside in one data center in a first geographic location while other components of the cluster reside in one or more other data centers in one or more other geographic locations that are potentially remote from the first geographic location. Thus, it is possible in some implementations of the system 100 for different ones of the compute nodes 102 to reside in different data centers than the content addressable storage system 105. Numerous other distributed implementations of one or both of the computer system 101 and the content addressable storage system 105 are possible. Accordingly, the content addressable storage system 105 can also be implemented in a distributed manner across multiple data centers.
It is to be appreciated that these and other features of illustrative embodiments are presented by way of example only, and should not be construed as limiting in any way.
Accordingly, different numbers, types and arrangements of system components such as computer system 101, compute nodes 102, network 104, content addressable storage system 105, storage devices 106, storage controller 108 and storage nodes 115 and 120 can be used in other embodiments.
It should be understood that the particular sets of modules and other components implemented in the system 100 as illustrated in
In the context of the
Referring now to
As indicated above, the storage system 505 in the present embodiment is assumed to comprise a content addressable storage system, although other types of storage systems can be used in other embodiments.
The source site data center 502 is coupled via one or more communication channels 520 of the network 504 to a target site data center 522 of the system 500. The target site data center 522 comprises a storage system 525. The storage system 525 comprises storage devices 526 and an associated storage controller 528. The storage controller 528 comprises replication control logic 532, snapshot generator 534 and signature generator 536.
The target site data center 522 further comprises a set of recovery servers 539 coupled to the storage system 525. The storage system 525, like the storage system 505, is assumed to comprise a content addressable storage system, although again other types of storage systems can be used in other embodiments.
The source site data center 502 and the target site data center 522 are examples of what are more generally referred to herein as respective ones of a “source site” and a “target site” of an information processing system. The source site data center 502 and the target site data center 522 will therefore also be referred to herein as respective source site 502 and target site 522 of the system 500. In some embodiments, the target site 522 comprises a disaster recovery site data center and the source site 502 comprises a production site data center, although other arrangements are possible.
The source site 502 and target site 522 may be implemented in respective distinct local and remote geographic locations, although it is also possible for the two sites to be within a common facility or even implemented on a common processing platform.
It is assumed that data is replicated in system 500 from the source site 502 to the target site 522 using asynchronous and synchronous replication modes. Typically, a given replication process will begin in an asynchronous replication mode, and will subsequently transfer from the asynchronous replication mode to a synchronous replication mode. For example, the asynchronous replication mode may be used to replicate the bulk of a given set of data from the first storage system to the second storage system. The mirroring functionality of the synchronous replication mode is then enabled. Other arrangements utilizing different replication modes are possible.
The synchronous replication mode in some embodiments is illustratively configured to mirror data writes between the first and second storage systems. For example, when a host device writes data to the first storage system, the first storage system responds to the host device with an acknowledgement of successful storage in the first storage system only after the first storage system sends the data to the second storage system and receives an acknowledgement of successful storage back from the second storage system.
The asynchronous replication mode in some embodiments implements cycle-based asynchronous replication to periodically transfer data in multiple cycles from the source site 502 to the target site 522. The data replicated from the source site 502 to the target site 522 can include all of the data stored in the storage system 505, or only certain designated subsets of the data stored in the storage system 505. Different replication processes of different types can be implemented for different parts of the stored data.
An exemplary cycle-based asynchronous replication process will now be described in more detail. Such a process is assumed to represent one possible implementation of an asynchronous replication mode of a replication process that includes both asynchronous and synchronous replications modes as well as support for concurrent operation of such modes and separate operation of the individual modes. The term “mode” as used herein in conjunction with asynchronous or synchronous replication may therefore itself comprise a corresponding asynchronous or synchronous replication process.
In order to conserve bandwidth on the communication channels 520 between the source site 502 and the target site 522, data is transferred incrementally in the asynchronous replication mode. This means that instead of sending all the data stored at the source site 502 to the target site 522 in each cycle, only the data that has been changed during each cycle is transferred. The changed data is an example of what is more generally referred to herein as “differential data.” A given set of differential data transferred from the source site 502 to the target site 522 in a given one of the cycles of the cycle-based asynchronous replication process represents a “delta” between a pair of source site snapshots generated by the snapshot generator 514 of the storage controller 508 for a corresponding pair of the cycles. Each source site snapshot captures the state at a particular point in time of the data to be replicated from the source site 502 to the target site 522. It is assumed that one such source site snapshot is generated by the snapshot generator 514 in conjunction with each of the cycles of the asynchronous replication process.
A given one the cycles of the cycle-based asynchronous replication process illustratively encompasses an amount of time spent sending a corresponding one of the sets of differential data or deltas from the source site 502 to the target site 522. There is a lag time between the data at the source site 502 and the replicated data at the target site 522. More particularly, the replicated data at the target site 522 is “older” than the data at the source site 502 by the lag time, as the production servers 519 continue to write to the storage system 505 after the source site snapshots are taken for respective ones of the cycles. For example, if the cycles of the cycle-based asynchronous replication process each take 30 seconds, then the lag time in some embodiments may vary between 30 seconds and 60 seconds. A recover point objective or RPO in some embodiments can be specified as a maximum amount of lag time that the replicated data can have.
The lag time in some embodiments is more particularly specified as an amount of time between initiation of transfer of a given one of the sets of differential data by the storage system 505 of the source site 502 and update of the corresponding target site snapshot by the storage system 525 of the target site 522. It is to be appreciated, however, that other specifications of the lag time can be used.
As noted above, an advantage of transferring data incrementally from the source site 502 to the target site 522 using a cycle-based asynchronous replication process is that it conserves bandwidth on the communication channels 520. For example, each byte of data written by the production servers 519 to the storage system 505 need only be transferred once. However, the downside is that if there is problem in any one of the cycles, the replicated data at the target site 522 will be corrupted from that point on. This is a silent corruption that without appropriate verification of the replicated data will not be discovered until recovery servers 539 are started and begin to utilize the replicated data in conjunction with disaster recovery or another similar type of recovery situation. It is therefore very important for the replicated data to be verified in an appropriate manner before such a recovery situation arises.
The production servers 519 at the source site 502 illustratively run applications for users of the system 500. These servers are configured to store application data in the storage system 505. This application data is illustratively part of the data stored in storage system 505 that is replicated from the source site 502 to the target site 522. The recovery servers 539 at the target site 522 are configured to take up the running of the applications for the users of the system 500 in the event of a disaster recovery or other recovery situation. The applications on the recovery servers 539 of the target site 522 are started using the data that has been replicated to the target site 522 in the cycle-based asynchronous replication process.
The production servers 519 and recovery servers 539 of the respective source site 502 and target site 522 illustratively comprise respective processing devices of one or more processing platforms of the corresponding source site 502 or target site 522. For example, these servers can comprise respective VMs each having a processor and a memory, although numerous other configurations are possible. At least portions of the source site 502 and target site 522 can be implemented in cloud infrastructure such as an AWS system or another cloud-based system such as GCP or Microsoft Azure.
As indicated previously, the storage systems 505 and 525 of the source and target sites 502 and 522 are configured in the present embodiment for automatic verification of asynchronously replicated data over multiple cycles of a cycle-based asynchronous replication process. This illustratively involves asynchronously replicating data from the storage devices 506 of the storage system 505 to the storage devices 526 of the storage system 525 and automatically verifying the correctness of portions of the replicated data over multiple cycles. As will be described in more detail below, the automatic verification of the asynchronously replicated data in the present embodiment may be performed in a manner that advantageously avoids the need to verify all of the transferred data in each cycle. As a result, the cycles can be made significantly more compact than would otherwise be possible. This results in enhanced efficiency in the replication process and thereby facilitates the achievement of recover point objectives in system 500.
As noted above, the storage systems 505 and 525 of the source and target sites 502 and 522 may comprise respective content addressable storage systems having respective sets of non-volatile memory storage devices.
Additionally or alternatively, the storage systems 505 and 525 of the source and target sites 502 and 522 may comprise respective clustered storage systems having respective sets of storage nodes each having a plurality of storage devices.
In some embodiments, the storage systems 505 and 525 illustratively comprise scale-out all-flash storage arrays such as XtremIO™ storage arrays from Dell EMC of Hopkinton, Mass. Other types of storage arrays, including by way of example Unity™, VNX® and Symmetrix VMAX® storage arrays also from Dell EMC, can be used to implement storage systems in other embodiments. A given such storage array can be configured to provide storage redundancy using well-known RAID techniques such as RAID 5 or RAID 6, although other storage redundancy configurations can be used.
The term “storage system” as used herein is therefore intended to be broadly construed, and should not be viewed as being limited to content addressable storage systems or flash-based storage systems.
The storage devices 506 and 526 of respective storage systems 505 and 525 illustratively implement a plurality of LUNs configured to store files, blocks, objects or other arrangements of data.
In the present embodiment, the storage system 525 of the target site 522 is configured to participate in a cycle-based asynchronous replication process with the storage system 505 of the source site 502. This cycle-based asynchronous replication process is illustratively implemented in system 500 by cooperative interaction of the storage systems 505 and 525 over network 504 using their respective replication control logic 512 and 532, snapshot generators 514 and 534, and signature generators 516 and 536. Examples of cycles of an illustrative cycle-based asynchronous replication process of this type will be described in more detail below.
The storage system 525 of the target site 522 is more particularly configured in this embodiment to receive from the storage system 505 of the source site 502, in respective ones of a plurality of cycles of the cycle-based asynchronous replication process, corresponding sets of differential data representing respective deltas between pairs of source site snapshots for respective pairs of the cycles. The source site snapshots are generated by the snapshot generator 514 of the storage controller 508.
The storage system 525 of the target site 522 illustratively utilizes the sets of differential data received in the respective ones of the cycles to update respective target site snapshots for those cycles. The target site snapshots are generated by the snapshot generator 534 of the storage controller 528.
Over multiple ones of the cycles, the storage system 525 of the target site 522 generates target site signatures for respective different portions of a designated one of the updated target site snapshots. The target site signatures are generated by the signature generator 536 of the storage controller 528. The storage system 525 also receives from the storage system 505 of the source site 502 corresponding source site signatures for respective different portions of a designated one of the source site snapshots. The source site signatures are generated by the signature generator 516 of the storage controller 508. The storage system 525 compares the target site and source site signatures over the multiple cycles in order to verify that the designated target site and source site snapshots are equivalent.
The verification of equivalence of the designated target site and source site snapshots in this embodiment is therefore spread over the multiple cycles, with pairs of target site and source site signatures for the different portions of those snapshots being compared in respective ones of the multiple cycles.
Terms such as “equivalent” and “equivalence” as used herein in conjunction with verification of replicated data by comparison of target site and source site snapshots are intended to be broadly construed to encompass various arrangements for confirming that the target site snapshot is an accurate and correct version of its corresponding source site snapshot. Such equivalence herein is a type of functional equivalence in that the replicated data when utilized by one or more applications running on the recovery servers 539 will produce the same results that would be produced by the corresponding source site data when utilized by one or more applications running on the production servers 519.
It is also important to note that the transferring of the data in cycles in this embodiment is separate from the verifying of the transferred data. The data transferred each cycle comprises the above-noted delta between two snapshots taken at respective ones of two different points in time. The data verification illustratively involves selecting a particular one of the target site snapshots, and then verifying the data in that snapshot over multiple cycles through the comparison of target site signatures for different portions of the selected target site snapshot to corresponding source site signatures. The transferred data comprising the deltas sent from the source site 502 to the target site 522 are not verified in each cycle.
The target site and source site signatures generated by the respective signature generators 516 and 536 illustratively comprise at least one of a checksum and a hash of corresponding portions of the designated target site and source site snapshots.
The different portions of the designated target site and source site snapshots for which the verification of equivalence is spread over the multiple cycles of the cycle-based asynchronous replication process may comprise respective percentages of the designated target site and source site snapshots. For example, different percentages of the designated target site and source site snapshots may be utilized in different ones of the multiple cycles. Alternatively, a fixed percentage of the designated target site and source site snapshots may be utilized in each of the multiple cycles. As a more particular example of the latter approach, the target site and source site signatures for different n percent portions of the designated target site and source site snapshots are verified in each of 100/n of the cycles.
In these and other embodiments, the different portions of the designated target site and source site snapshots for which the verification of equivalence is spread over the multiple cycles can be determined at least in part based on a number n of the cycles of the cycle-based asynchronous replication process that are expected to be executed within a given time period. For example, the different portions of the designated target site and source site snapshots for which the verification of equivalence is spread over the multiple cycles may be determined by first determining the expected number of cycles n for the given time period and then computing 100/n to determine a percentage of the designated target site and source site snapshots to be verified in each of the n cycles.
Additionally or alternatively, the different portions of the designated target site and source site snapshots for which the verification of equivalence is spread over the multiple cycles can be dynamically adapted over time in order to control a lag time between initiation of transfer of a given one of the sets of differential data by the storage system 505 of the source site 502 and update of the corresponding target site snapshot by the storage system 525 of the target site 522.
For example, such dynamic adaptation can be implemented by, for a current one of the multiple cycles, calculating a verification rate as a function of a time elapsed for verification of a given one of the portions in a previous one of the multiple cycles, calculating an amount of time remaining in a recover point objective period for the current cycle, and multiplying the verification rate by the amount of time remaining in the recover point objective period for the current cycle to determine a particular portion of the designated target site and source site snapshots to be verified in the current cycle.
If the particular portion determined by multiplying the verification rate by the amount of time remaining in the recover point objective period for the current cycle is less than a specified minimum portion, the minimum portion is verified in the current cycle.
Further details regarding automatic verification of asynchronously replicated data suitable for use in illustrative embodiments herein can be found in U.S. patent application Ser. No. 15/662,809, filed Jul. 28, 2017 and entitled “Automatic Verification of Asynchronously Replicated Data,” which is incorporated by reference herein. Other embodiments need not utilize these automatic verification techniques, and can be implemented using alternative verification techniques as well as other types of replication processes. Accordingly, illustrative embodiments herein are not limited to use with cycle-based asynchronous replication, but are more generally applicable to other types of data replication.
The particular exemplary cycle-based asynchronous replication processes described above can be varied in other embodiments. Alternative synchronous replication processes may also be used. As mentioned previously, such processes are performed in respective asynchronous and synchronous replication modes of a replication process that incorporates both asynchronous and synchronous replication.
Each of the source site 502 and target site 522 in the
As a more particular example, the storage controllers 508 and 528 or various components thereof can each be implemented in the form of one or more LXCs running on one or more VMs. Other arrangements of one or more processing devices of a processing platform can be used to implement the storage controllers 508 and 528 and/or their respective components. Other portions of the system 500 can similarly be implemented using one or more processing devices of at least one processing platform.
The source site 502 and target site 522 are illustratively implemented on respective distinct processing platforms, although numerous other arrangements are possible. For example, in some embodiments at least portions of the source site 502 and the target site 522 may be implemented on the same processing platform. The term “processing platform” as used herein is intended to be broadly construed so as to encompass, by way of illustration and without limitation, multiple sets of processing devices and associated storage systems that are configured to communicate over one or more networks.
Again, it is to be appreciated that these and other features of illustrative embodiments are presented by way of example only, and should not be construed as limiting in any way. Accordingly, different numbers, types and arrangements of system components such as source and target sites 502 and 522 and their respective storage systems 505 and 525 and storage controllers 508 and 528 can be used in other embodiments. In these other embodiments, only subsets of these components, or additional or alternative sets of components, may be used, and such components may exhibit alternative functionality and configurations.
The replication process carried out between the source site storage system 505 and the target site storage system 525 in the
Referring now to
In another example embodiment the method identifies the prerequisite condition as, for example, CPU usage. For example, there may be plenty of CPU bandwidth, and/or the CPU usage may be below a threshold. In an example embodiment, the source site data center 502 receives a multi-page replication request. In another example embodiment, the source site data center 502 receives a plurality of multi-page replication requests. In an example embodiment, the multi-page replication request comprises a list of addresses and their respective page pointers. For example, the page pointer may be hash handles or pointers to back-end physical storage. The method determines that at least one replication condition (i.e., the above-mentioned pre-requisite conditions) meets a threshold. In response, the method optimizes the multi-page replication request and/or the plurality of multi-page replication requests to optimize the replication process. For example, the source site data center 502 receives the multi-page replication request, comprising pages for addresses A100, A101, A201, A104, A107, A108, A110. The method groups the pages into several groups. For example, the method may group the pages according to adjacent addresses. In this example embodiment, the method groups pages for addresses A100, A101, A201, A104, A107, A108, A110 into four segments; Segment A={A100, A101, A102}, Segment B={A104}, Segment C={A107, A108}, and Segment D=(A110).
In an example embodiment, if any of the pages are compressed, the method decompresses the page. In another example embodiment, if CPU capacity is at a premium, the method may decide not to decompress a compressed page, for example, if the compression rate of the compressed page is a good compression ratio. For example, a compressed page that has a compression ratio of 40% or 55% of the original page already has a good compression ratio. If CPU capacity is at a premium, then it may not be advantageous to expend more CPU to decompress the compressed pages.
The method then combines all the pages in each of segment into one contiguous segment. In other words, the pages in Segment A {A100, A101, A102} are combined into one contiguous segment, and the pages in Segment C {A107, A108} are also combined into one contiguous segment. Thus, if a data page is 4 KB in size, Segment A is 12 KB, Segment B is 4 KB, Segment C is 8 KB and Segment D is 4 KB. The method then compresses each segment as a single entity. Thus, when nearby pages are grouped into the same segment, and compressed together, the compression ratio increases without over-inflating the compression dictionary. Compression methods often use a compression dictionary that describes how to compress different segments of data. Compression may be more efficient when similar data is compressed together, and less efficient when two pieces of unrelated data are compressed together. In the latter case, the compression dictionary may get over inflated, since it may be comprised of a dictionary for the first segment merged with a dictionary for the second segment. Further, nearby pages are more likely to have similar data than random pages (for example, database records with data that varies very little from record to record). For example, pages that are in nearby locations in a storage volume are more likely to have similar data than pages that are far apart in location, or in different volumes. Two pages with similar data compress (when grouped together) with a higher compression ratio than each page separately. Additionally, a page (i.e., page “X”) that does not have a good compression rate, for example 95% of its original size when compressed singularly, will achieve a higher compression rate when page “X” is grouped with another page that has similar data (for example, adjacent page “X+1”) and the two pages are compressed together. The method then transmits the compressed segments Segment A, Segment B, Segment C, and Segment D from the source site data center 502 to the target site data center 522. At the target site data center 522, the method decompresses each segment and separates each segment back into the pages. Thus, Segment A is decompressed and separated into pages A100, A101, A102, Segment B is decompressed into page A104, Segment C is decompressed and separated into pages A107, A108, and Segment D is decompressed into page A110. The target site data center 522 then stores the data pages in its back end. In an example embodiment, the target site data center 522 may compress the data pages again.
The operation of managing asynchronous replication will now be described in further detail with reference to the flow diagram of the illustrative embodiment of
In an example embodiment, at 701, the method determines at least one replication condition meets a threshold. In an example embodiment, the replication condition includes Central Processing Unit (CPU) usage. For example, there may be plenty of CPU bandwidth available. In another example embodiment, the CPU usage may be below a threshold.
In an example embodiment, the replication condition includes replication bandwidth. For example, as noted above, the IP links associated with each routing module 108R may have large queues and/or the replication lag may be above a threshold. In another example embodiment, bandwidth may be limited and/or expensive.
At 702, the method, in response, optimizes the multi-page replication request to optimize the replication process, for example, the method optimizes the multi-page replication request to minimize bandwidth required to complete the replication process. This is especially helpful when bandwidth is at a premium. In another example embodiment, the method optimizes data compression in situations where there is plenty of available CPU. For example, the method may detect a large amount of available CPU when the CPU usage is below a threshold.
In an example embodiment, when the method receives a multi-page replication request, the method detects that at least one page of received the multi-page replication request is compressed, and decompresses the compressed page(s). In another example embodiment, the method may choose not to decompress the compressed pages of the multi-page replication request. For example, if one page of the multi-page replication request is compressed to 40% of the original page size and another page of the multi-page replication request is compressed to 55% of the original page size, then the method may choose not to decompress (and then re-compress after the pages are grouped together) since the compression rate of both pages is relatively high. Alternatively, the method may choose not to decompress the compressed pages of the multi-page replication request if, for example, the CPU usage is above a threshold. In other words, the method chooses not to expend CPU bandwidth on decompressing, grouping and then compressing pages of the multi-page replication request based on, for example, CPU usage, compression rates of compressed pages of the multi-page replication request, etc.
In an example embodiment, the method groups at least two pages of the multi-page replication request into a contiguous segment, and performs compression on the segment. In another example embodiment, the method groups at least two pages of a plurality of multi-page replication requests into a contiguous segment, and performs compression on the segment. For example, the method may group the pages in one multi-page replication request, or the method may group the pages in one or more multi-page replication requests.
In an example embodiment, the multi-page replication request is comprised of a plurality of addresses, where each of the plurality of addresses has a respective page pointer. In this example embodiment, the method groups two or more pages of the multi-page replication request according to addresses associated with at least two pages. In an example embodiment, the addresses associated with at least two pages are adjacent addresses. In this example embodiment, as illustrated in
In an example embodiment, the method groups at least two pages of the multi-page replication request according to an optimum page size. In an example embodiment, the optimum page size correlates to an optimum compression ratio. For example, the method may determine a “sweet spot” page size for improving or maximizing compression rate. In this example embodiment, the method groups the pages of the multi-page replication request according to the optimum page size prior to compressing each group as a contiguous segment.
In an example embodiment, the method groups at least two pages of the multi-page replication request according to a compression ratio associated with each of at least two pages of the multi-page replication request. For example, the method may determine that compressed pages having similar compression rates may have similar data, and therefore would result in an optimized compression rate if those pages were decompressed, grouped together as a contiguous segment, and then compressed as a contiguous segment.
In an example embodiment, grouping at least two pages of the multi-page replication request results in an improved compression ratio over a respective compression ratio associated with each of at least two pages of the multi-page replication request. For example, a page that has a compression rate of 95% may have a higher compression rate when grouped with another page that has, for example, similar data. For example, the page that has a compression rate of 95% individually, may have a compression rate of 60% when grouped with another page for example, a page with an adjacent address and/or a page containing similar data.
In an example embodiment, method receives, at the second storage system, the compressed segment. The method decompresses the compressed segment into a plurality of decompressed pages, and compresses at least one of the plurality of decompressed pages. For example, as illustrated in
It is also to be appreciated that the
The particular processing operations and other system functionality described in conjunction with the flow diagrams of
Functionality such as that described in conjunction with the flow diagrams of
For example, a storage controller such as storage controller 108, 508, or 528 that is configured to control performance of one or more steps of the
In some embodiments, the first and second storage systems comprise respective XtremIO™ storage arrays suitably modified to incorporate managing asynchronous replication techniques as disclosed herein. As described previously, in the context of an XtremIO™ storage array, the control modules 108C, data modules 108D, routing modules 108R and management module(s) 108M of the distributed storage controller 108 in system 100 illustratively comprise C-modules, D-modules, R-modules and SYM module(s), respectively. These exemplary processing modules of the distributed storage controller 108 can be configured to implement managing asynchronous replication using the
The asynchronous replication management techniques implemented in the embodiments described above can be varied in other embodiments. For example, different types of process operations can be used in other embodiments. Furthermore, although described in some embodiments in the context of data replication from a source to a target, the asynchronous replication management techniques in other embodiments can be implemented in the context of other types of data transfer within a given storage system or from one storage system to another storage system.
In addition, the above-described functionality associated with C-module, D-module, R-module and SYM module components of an XtremIO™ storage array can be incorporated into other processing modules or components of a centralized or distributed storage controller in other types of storage systems.
Illustrative embodiments of content addressable storage systems or other types of storage systems with functionality for managing asynchronous replication as disclosed herein can provide a number of significant advantages relative to conventional arrangements.
For example, some embodiments can advantageously provide significantly improved efficiency in data replication processes carried out between a source site and a target site of a given information processing system.
One or more such embodiments are configured to minimize replication bandwidth for asynchronous replication requests.
Additionally, some embodiments can advantageously optimize data compression.
These and other embodiments include clustered storage systems comprising storage controllers that are distributed over multiple storage nodes. Similar advantages can be provided in other types of storage systems.
It is to be appreciated that the particular advantages described above and elsewhere herein are associated with particular illustrative embodiments and need not be present in other embodiments. Also, the particular types of information processing system features and functionality as illustrated in the drawings and described above are exemplary only, and numerous other arrangements may be used in other embodiments.
As mentioned previously, at least portions of the information processing systems 100 and 500 may be implemented using one or more processing platforms. A given such processing platform comprises at least one processing device comprising a processor coupled to a memory. The processor and memory in some embodiments comprise respective processor and memory elements of a virtual machine or container provided using one or more underlying physical machines. The term “processing device” as used herein is intended to be broadly construed so as to encompass a wide variety of different arrangements of physical processors, memories and other device components as well as virtual instances of such components. For example, a “processing device” in some embodiments can comprise or be executed across one or more virtual processors. Processing devices can therefore be physical or virtual and can be executed across one or more physical or virtual processors. It should also be noted that a given virtual device can be mapped to a portion of a physical one.
Some illustrative embodiments of a processing platform that may be used to implement at least a portion of an information processing system comprise cloud infrastructure including virtual machines implemented using a hypervisor that runs on physical infrastructure. The cloud infrastructure further comprises sets of applications running on respective ones of the virtual machines under the control of the hypervisor. It is also possible to use multiple hypervisors each providing a set of virtual machines using at least one underlying physical machine. Different sets of virtual machines provided by one or more hypervisors may be utilized in configuring multiple instances of various components of the system.
These and other types of cloud infrastructure can be used to provide what is also referred to herein as a multi-tenant environment. One or more system components such as storage systems 105, 505, and 525, or portions thereof, are illustratively implemented for use by tenants of such a multi-tenant environment.
As mentioned previously, cloud infrastructure as disclosed herein can include cloud-based systems such as AWS, GCP and Microsoft Azure. Virtual machines provided in such systems can be used to implement at least portions of one or more of a computer system and a content addressable storage system in illustrative embodiments. These and other cloud-based systems in illustrative embodiments can include object stores such as Amazon S3, GCP Cloud Storage, and Microsoft Azure Blob Storage.
In some embodiments, the cloud infrastructure additionally or alternatively comprises a plurality of containers implemented using container host devices. For example, a given container of cloud infrastructure illustratively comprises a Docker container or other type of LXC. The containers may run on virtual machines in a multi-tenant environment, although other arrangements are possible. The containers may be utilized to implement a variety of different types of functionality within the system 100 or 500. For example, containers can be used to implement respective processing devices providing compute and/or storage services of a cloud-based system. Again, containers may be used in combination with other virtualization infrastructure such as virtual machines implemented using a hypervisor.
Illustrative embodiments of processing platforms will now be described in greater detail with reference to
Although only a single hypervisor 804 is shown in the embodiment of
An example of a commercially available hypervisor platform that may be used to implement hypervisor 804 and possibly other portions of the information processing system 100 in one or more embodiments is the VMware® vSphere® which may have an associated virtual infrastructure management system such as the VMware® vCenter™. The underlying physical machines may comprise one or more distributed processing platforms that include one or more storage systems.
As is apparent from the above, one or more of the processing modules or other components of system 100 may each run on a computer, server, storage device or other processing platform element. A given such element may be viewed as an example of what is more generally referred to herein as a “processing device.” The cloud infrastructure 800 shown in
The processing platform 900 in this embodiment comprises a portion of system 100 and includes a plurality of processing devices, denoted 902-1, 902-2, 902-3, . . . 902-K, which communicate with one another over a network 904.
The network 904 may comprise any type of network, including by way of example a global computer network such as the Internet, a WAN, a LAN, a satellite network, a telephone or cable network, a cellular network, a wireless network such as a WiFi or WiMAX network, or various portions or combinations of these and other types of networks.
The processing device 902-1 in the processing platform 900 comprises a processor 910 coupled to a memory 912.
The processor 910 may comprise a microprocessor, a microcontroller, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other type of processing circuitry, as well as portions or combinations of such circuitry elements.
The memory 912 may comprise random access memory (RAM), read-only memory (ROM) or other types of memory, in any combination. The memory 912 and other memories disclosed herein should be viewed as illustrative examples of what are more generally referred to as “processor-readable storage media” storing executable program code of one or more software programs.
Articles of manufacture comprising such processor-readable storage media are considered illustrative embodiments. A given such article of manufacture may comprise, for example, a storage array, a storage disk or an integrated circuit containing RAM, ROM or other electronic memory, or any of a wide variety of other types of computer program products. The term “article of manufacture” as used herein should be understood to exclude transitory, propagating signals. Numerous other types of computer program products comprising processor-readable storage media can be used.
Also included in the processing device 902-1 is network interface circuitry 914, which is used to interface the processing device with the network 904 and other system components, and may comprise conventional transceivers.
The other processing devices 902 of the processing platform 900 are assumed to be configured in a manner similar to that shown for processing device 902-1 in the figure.
Again, the particular processing platform 900 shown in the figure is presented by way of example only, and system 100 may include additional or alternative processing platforms, as well as numerous distinct processing platforms in any combination, with each such platform comprising one or more computers, servers, storage devices or other processing devices.
For example, other processing platforms used to implement illustrative embodiments can comprise different types of virtualization infrastructure, in place of or in addition to virtualization infrastructure comprising virtual machines. Such virtualization infrastructure illustratively includes container-based virtualization infrastructure configured to provide Docker containers or other types of LXCs.
As another example, portions of a given processing platform in some embodiments can comprise converged infrastructure such as VxRail™, VxRack™, VxRack™ FLEX, VxBlock™, or Vblock® converged infrastructure from VCE, the Virtual Computing Environment Company, now the Converged Platform and Solutions Division of Dell EMC.
It should therefore be understood that in other embodiments different arrangements of additional or alternative elements may be used. At least a subset of these elements may be collectively implemented on a common processing platform, or each such element may be implemented on a separate processing platform.
Also, numerous other arrangements of computers, servers, storage devices or other components are possible in the information processing system 100. Such components can communicate with other elements of the information processing system 100 over any type of network or other communication media.
As indicated previously, components of an information processing system as disclosed herein can be implemented at least in part in the form of one or more software programs stored in memory and executed by a processor of a processing device. For example, at least portions of the functionality of one or more components of the storage controllers 108, 508, and 528 of systems 100 and 500 are illustratively implemented in the form of software running on one or more processing devices.
It should again be emphasized that the above-described embodiments are presented for purposes of illustration only. Many variations and other alternative embodiments may be used. For example, the disclosed techniques are applicable to a wide variety of other types of information processing systems, source and target sites, storage systems, storage nodes, storage devices, storage controllers, replication processes, replication engines and associated control logic. Also, the particular configurations of system and device elements and associated processing operations illustratively shown in the drawings can be varied in other embodiments. Moreover, the various assumptions made above in the course of describing the illustrative embodiments should also be viewed as exemplary rather than as requirements or limitations of the disclosure. Numerous other alternative embodiments within the scope of the appended claims will be readily apparent to those skilled in the art.
