A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document and/or the patent disclosure as it appears in the United States Patent and Trademark Office patent file and/or records, but otherwise reserves all copyrights whatsoever.
Businesses recognize the commercial value of their data and seek reliable, cost-effective ways to protect the information stored on their computer networks while minimizing impact on productivity. A company might back up critical computing systems such as databases, file servers, web servers, virtual machines, and so on as part of a routine schedule. Given the rapidly expanding volume of data under management, companies also continue to seek innovative techniques for managing data growth, for example by migrating data to lower-cost storage over time, reducing redundant data, pruning lower priority data, adopting cloud storage, etc.
Dramatic increases in the use of public cloud computing environments (e.g., Microsoft Azure, Amazon Web Services, Google Cloud, etc.) present certain barriers to cloud service subscribers' ability to exercise command and control over cloud-based virtual machines (VM), particularly in regard to making and later accessing backup copies of the VMs. Thus, a need arises for technologies that streamline the use of cloud subscribers' cloud-based VMs.
In the context of backups and data protection, “Live Mounting” a virtual machine (VM) causes the VM to run off a backup copy that might have been previously taken of a “live” production VM. The “live-mounted VM” is generally intended for temporary use. One typical use is to validate the integrity and contents of the backup copy, such as for disaster recovery validation. Another typical use is to access some contents of the backup copy from the live-mounted VM without restoring all backed up files. These uses contemplate that changes occurring during live mount are not preserved after the live-mounted VM expires or is taken down. Thus, live mounting a VM is not a restore operation and usually does not involve access to every block of data in the backup copy. However, the prior art does not support live mounting a VM in a public cloud computing environment from a non-cloud-native backup copy, i.e., from a backup copy created by a backup system other than (non-native to) the cloud computing environment that hosts the live-mounted VM. The backup copy is thus in a proprietary format of the backup system. The present disclosure presents a technological solution that overcomes the deficiencies of the prior art.
“Live Recovery” of a VM provides a longer-term platform than live mount. Live recovery generates a new “recovery VM” that operates as an ongoing “live” production platform. The previously created non-cloud-native backup copy is the data source for the recovery VM. Live recovery causes data blocks from the backup copy to be moved (restored, recovered, retrieved, recalled) from the backup copy on backup media to cloud-based virtual disk(s) assigned to the recovery VM. The movement operation methodically transfers all portions of the backup copy to the cloud-based virtual disk(s). As a result, the cloud-based recovery VM can become fully operational in the cloud computing environment on a going-forward basis. The advantage of live recovery over a traditional restore operation is that live recovery enables a cloud-based VM to begin operating well before the backup copy is fully restored. This is accomplished by temporarily mounting a VM (the “temp-mounted VM”) in the cloud while the backup copy is being restored in the background. VM reads and writes begin issuing from the temp-mounted VM and writes are not lost on completion. However, the prior art does not support live recovery of a VM in a public cloud computing environment from a non-cloud-native backup copy. The present disclosure presents a technological solution that overcomes this deficiency.
A “cloud computing environment” as used herein comprises resources provided as a service by a cloud service provider to a cloud service account. A cloud computing environment is accessed via the cloud service account, which entitles the subscriber to use services supplied by the cloud service provider. Cloud computing environments vary among cloud services, among cloud availability zones, and even among cloud service accounts from the same cloud service provider. A cloud computing environment as used herein comprises data processing (computing) resources such as cloud-based VMs, and further comprises data storage resources that host virtual disks acting as datastores for the cloud-based VMs.
VM Live Mount in a Public Cloud Computing Environment.
Public cloud computing environments are controlled by cloud service providers, and techniques in the prior art for live mounting VMs from a non-cloud-native backup copy do not work in public cloud computing environments. In part this is because public cloud computing environments do not allow VMs to boot from a non-cloud-native external source, such as a source that hosts the backup copy. The disclosed live mount feature overcomes this problem. An illustrative data storage management system generates VM backup copies and stores them to a proprietary data store, which may be located in a non-cloud data center or in a cloud storage environment of the same or different cloud service provider that hosts the live-mounted VM. The data storage management system comprises technologies for enabling cloud-based VMs to be live-mounted off the backup copies in the proprietary data store. This technological improvement enables a number of beneficial uses of live-mounted cloud-based VMs, e.g., checking backup integrity, checking backup content, making temporary use of backed up data, testing a disaster recovery scenario, etc., without limitation. Yet the illustrative system makes possible all these uses without requiring the backup copy to be restored to the VM's cloud host. Instead, portions of the VM backup copies are recalled only as needed by the live-mounted VM.
VM Live Recovery in a Public Cloud Computing Environment.
Public cloud computing environments are controlled by cloud service providers and do not allow VMs to boot from a non-cloud-native external source, such as a source that hosts the backup copy, and also do not allow data from such backup copies to be moved from a non-native external source onto a cloud-based virtual disk without using a restore operation. As a consequence, to recover or move a VM to a public cloud computing environment, the prior art requires restoring the backup copy in its entirety to the cloud destination. Although the restore operation is effective to make all data from the backup copy available to a cloud-based VM, the cloud-based VM cannot become operational until the restore operation completes. This prior art approach causes substantial delays, particularly if the amount of data to be restored is very large or involves many virtual disks. In disaster recovery or VM migration scenarios, such delays are highly unsatisfactory, and a faster disaster recovery time is desired. The disclosed live recovery feature overcomes this deficiency. The illustrative data storage management system enables cloud-based VMs to be live recovered off backup copies in the proprietary data store. This technological improvement provides faster VM recovery time and less downtime because a cloud-based VM can begin operating off the backup copy without waiting for a restore operation to complete; instead, the cloud-based VM issues reads and writes even while the restore operation is still in progress. A novel data mover process thread causes all portions of the backup copy to move directly to cloud-based virtual disks assigned to the recovery VM. During the course of the move, a temporarily operating VM is mounted (the temp-mounted VM) and issues reads and writes. After the move of the backup copy to the virtual disk(s) is complete, i.e., after the restore operation ends, the temp-mounted VM is powered off and communicative connections are severed to the proprietary components of the illustrative system that implemented the move and sustained the temp-mounted VM during the move. At this point, the cloud-based virtual disk(s) have been populated with all of the backup copy and additionally also comprise all writes issued by the temp-mounted VM. Therefore, each virtual disk is current and ready to serve the recovery VM, which is configured as a more persistent cloud-based VM that will run going forward. The illustrative system boots up the recovery VM in the cloud computing environment, connects it to the fully populated virtual disk(s) that form its datastore, and thereafter the recovery VM resumes read and write operations. There is only minimal downtime during the switchover from temp-mounted VM to recovery VM.
Differences Between Live Mount and Live Recovery.
The live recovery feature leverages some but not all of the techniques used for live mounting a cloud-based VM, and therefore live recovery operates as a separate feature in the illustrative system. One key difference is that live recovery moves data from the data source (e.g., backup copy) directly to the cloud-based virtual disks of the recovery VM, thus minimizing intermediate copy operations. Another key difference is that live recovery methodically moves all of the backup copy to the cloud computing environment, whereas live mount moves data responsive to on-demand read requests but does not perform a complete restore cycle. Another key difference is that data written during live recovery is preserved for use by the recovery VM at the cloud-based virtual disks, whereas the temporary nature of live mount discards writes after live-mounting ends. Furthermore, live recovery arbitrates among simultaneous read requests when an on-demand read request from the temp-mounted VM coincides with a recall from backup media for the same data. This arbitration feature is key to sustaining active reads while the restore is still in progress, which is one of the advantages of live recovery over traditional restores.
Enhanced Data Storage Management System.
The illustrative data storage management system generates VM backup copies, whether from a non-cloud data center or from cloud-based VMs. The data storage management system stores and maintains the backup copies in a proprietary data store, which may or may not reside in the present cloud computing environment. The data storage management system keeps one or more indexes that track where each backup copy is stored and further tracks where each portion of a backup copy (e.g., extents, chunks, data blocks, etc.) are stored on backup media. The indexing capability enables intelligent pin-point retrieval and staging of data from backup media when live mounting and/or live recovering a VM. For simplicity and as a shorthand usage herein, portions of a backup copy (or of a VM snapshot) are referred to herein as “extents,” though it will be understood that the term may be interchangeable with “portions,” “chunks,” or “data blocks,” depending on how the system is implemented. Some of the illustrative components of the data storage management system operate in the same cloud computing environment as the VM that is to be live mounted and/or live recovered, whereas other components operate in another distinct cloud computing environment, in another cloud availability zone, or in a non-cloud data center, without limitation. Accordingly, the illustrative live mount and live recovery features can be implemented in a variety of configurations, such as non-cloud data center-to-cloud, cloud-to-cloud, multi-cloud, etc.
The illustrative data storage management system comprises a specially-configured computing device that operates in the cloud computing environment that hosts the live-mounted VM and/or recovery VM. The specially-configured computing device is communicatively interposed between a live-mounted/temp-mounted VM and a media agent component of the illustrative system. In some embodiments, the interposed computing device is known as a “Linux File Recovery Enabler” (FREL), for interoperating with cloud-based VMs that have a Linux operating system, but the invention is not so limited. As a shorthand, the interposed computing device is referred to herein as the “recovery computing device.”
The media agent is communicatively coupled to storage resources where the backup copy is stored (the “backup media” or “backup storage”) as well as to the recovery computing device. The media agent comprises indexing information (e.g., a backup index) that tracks where the backup copy's extents are stored on backup media. The recovery computing device comprises a pseudo-disk driver that executes thereon and intercepts reads and writes issued by the live-mounted/temp-mounted VM. For live recovery, the pseudo-disk driver also interoperates with cloud-based virtual disk(s) assigned to the recovery VM. The pseudo-disk driver presents a pseudo-disk as a block storage device for receiving and serving data. The pseudo-disk driver places data retrieved by the media agent from the backup copy to a logically-defined “recall store” in the pseudo-disk. The pseudo-disk driver places write data received from the live-mounted/temp-mounted VM to a logically-defined “private store” in the pseudo-disk.
For live mount, the pseudo-disk driver configures the private store and recall store at the recovery computing device. The pseudo-disk driver intercepts read requests issued by the live-mounted VM and preferentially serves them from the private store or the recall store at the recovery computing device. If a read request cannot be served therefrom, data is retrieved from the backup copy and stored to the recall store and then served to the VM. In this way, the recall store is populated over time from on-demand read requests issued by the live-mounted VM. Ultimately, data in the private store at the recovery computing device will be discarded when the live-mounted VM expires or is taken down.
For live recovery, the pseudo-disk driver configures the private store and recall store at the cloud-based virtual disk(s) that will ultimately serve as the recovery VM's datastore. This aspect is distinguishable from the expendable pseudo-disk created for the live mount feature at the recovery computing device. A novel data mover process thread that runs at the recovery computing device causes the backup copy to be sequentially traversed to move extents directly to the cloud-based virtual disk(s). The pseudo-disk driver preferentially serves on-demand reads from the private store or the recall store at the cloud-based virtual disk, but initially those stores lack the requested extents. The pseudo-disk driver maintains an input-output (I/O) bitmap that tracks whether each extent in the backup copy has been successfully recalled from the backup copy to ensure that all of the backup copy is recovered and its extents stored to the cloud-based virtual disk(s). Because the cloud-based virtual disk will persist as the recovery VM's datastore, all on-demand reads and writes that issue from the temp-mounted VM are directly applied to the cloud-based virtual disk(s). The data mover arbitrates when an on-demand read arrives for an extent that is currently being recalled from backup media. In such a case, the data mover allows the recall to complete before the on-demand read request is served from the cloud-based virtual disk(s). This logic ensures that there are no double-takes on recalls from backup media.
Other salient aspects of the illustrative system include a pre-packaged pool of machine images or operating system images (e.g., Amazon machine image (AMI), etc.). This “machine image pool” is configured in the VM host cloud as a persistent resource for future live mount and live recovery operations. Each image comprises utilities for loading a root file system into a target VM such as the live-mounted VM or the temp-mounted VM, and further comprises key drivers for setting up the VM's virtual disk, such as a Network File System (NFS) driver and a network interface card (NIC) driver, which is accompanied by a script for registering Internet Small Computer Systems Interface (iSCSI) disks. The machine image pool is tapped when a VM is to be live-mounted or temp-mounted. Accordingly, the system selects a proper machine image that is suitable to the configuration of the target VM, e.g., having a certain version of a certain operating system, etc.
Another salient aspect of the illustrative system is a recovery process that executes on the recovery computing device. The recovery process coordinates certain aspects of the disclosed features, such as invoking a pseudo-disk driver corresponding to each VM virtual disk, causing the pseudo-disk driver to set up the corresponding pseudo-disk, selecting a suitable machine image from the machine image pool, causing a VM to be instantiated for the live-mount/temp-mount operation. In the live recovery operation, the recovery process additionally coordinates the switchover from temp-mounted VM to recovery VM. More details are given in the accompanying figures and in the sections entitled Live Mount Of Virtual Machines in a Public Cloud Computing Environment and Live Recovery Of Virtual Machines in a Public Cloud Computing Environment below.
Detailed descriptions and examples of systems and methods according to one or more illustrative embodiments of the present invention may be found in the sections entitled Live Mount Of Virtual Machines in a Public Cloud Computing Environment and Live Recovery Of Virtual Machines in a Public Cloud Computing Environment below, as well as in the section entitled Example Embodiments, and also in
Various embodiments described herein are intimately tied to, enabled by, and would not exist except for, computer technology. For example, the transfer of extents from backup copy to the interposed computing device and from there to a cloud-based virtual disk as described herein in reference to various embodiments cannot reasonably be performed by humans alone, without the computer technology upon which they are implemented.
Information Management System Overview
With the increasing importance of protecting and leveraging data, organizations simply cannot risk losing critical data. Moreover, runaway data growth and other modern realities make protecting and managing data increasingly difficult. There is therefore a need for efficient, powerful, and user-friendly solutions for protecting and managing data and for smart and efficient management of data storage. Depending on the size of the organization, there may be many data production sources which are under the purview of tens, hundreds, or even thousands of individuals. In the past, individuals were sometimes responsible for managing and protecting their own data, and a patchwork of hardware and software point solutions may have been used in any given organization. These solutions were often provided by different vendors and had limited or no interoperability. Certain embodiments described herein address these and other shortcomings of prior approaches by implementing scalable, unified, organization-wide information management, including data storage management.
Generally, the systems and associated components described herein may be compatible with and/or provide some or all of the functionality of the systems and corresponding components described in one or more of the following U.S. patents/publications and patent applications assigned to Commvault Systems, Inc., each of which is hereby incorporated by reference in its entirety herein:
System 100 includes computing devices and computing technologies. For instance, system 100 can include one or more client computing devices 102 and secondary storage computing devices 106, as well as storage manager 140 or a host computing device for it. Computing devices can include, without limitation, one or more: workstations, personal computers, desktop computers, or other types of generally fixed computing systems such as mainframe computers, servers, and minicomputers. Other computing devices can include mobile or portable computing devices, such as one or more laptops, tablet computers, personal data assistants, mobile phones (such as smartphones), and other mobile or portable computing devices such as embedded computers, set top boxes, vehicle-mounted devices, wearable computers, etc. Servers can include mail servers, file servers, database servers, virtual machine servers, and web servers. Any given computing device comprises one or more processors (e.g., CPU and/or single-core or multi-core processors), as well as corresponding non-transitory computer memory (e.g., random-access memory (RAM)) for storing computer programs which are to be executed by the one or more processors. Other computer memory for mass storage of data may be packaged/configured with the computing device (e.g., an internal hard disk) and/or may be external and accessible by the computing device (e.g., network-attached storage, a storage array, etc.). In some cases, a computing device includes cloud computing resources, which may be implemented as virtual machines. For instance, one or more virtual machines may be provided to the organization by a third-party cloud service vendor.
In some embodiments, computing devices can include one or more virtual machine(s) running on a physical host computing device (or “host machine”) operated by the organization. As one example, the organization may use one virtual machine as a database server and another virtual machine as a mail server, both virtual machines operating on the same host machine. A Virtual machine (“VM”) is a software implementation of a computer that does not physically exist and is instead instantiated in an operating system of a physical computer (or host machine) to enable applications to execute within the VM's environment, i.e., a VM emulates a physical computer. AVM includes an operating system and associated virtual resources, such as computer memory and processor(s). A hypervisor operates between the VM and the hardware of the physical host machine and is generally responsible for creating and running the VMs. Hypervisors are also known in the art as virtual machine monitors or a virtual machine managers or “VMMs”, and may be implemented in software, firmware, and/or specialized hardware installed on the host machine. Examples of hypervisors include ESX Server, by VMware, Inc. of Palo Alto, Calif.; Microsoft Virtual Server and Microsoft Windows Server Hyper-V, both by Microsoft Corporation of Redmond, Wash.; Sun xVM by Oracle America Inc. of Santa Clara, Calif.; and Xen by Citrix Systems, Santa Clara, Calif. The hypervisor provides resources to each virtual operating system such as a virtual processor, virtual memory, a virtual network device, and a virtual disk. Each virtual machine has one or more associated virtual disks. The hypervisor typically stores the data of virtual disks in files on the file system of the physical host machine, called virtual machine disk files (“VMDK” in VMware lingo) or virtual hard disk image files (in Microsoft lingo). For example, VMware's ESX Server provides the Virtual Machine File System (VMFS) for the storage of virtual machine disk files. A virtual machine reads data from and writes data to its virtual disk much the way that a physical machine reads data from and writes data to a physical disk. Examples of techniques for implementing information management in a cloud computing environment are described in U.S. Pat. No. 8,285,681. Examples of techniques for implementing information management in a virtualized computing environment are described in U.S. Pat. No. 8,307,177.
Information management system 100 can also include electronic data storage devices, generally used for mass storage of data, including, e.g., primary storage devices 104 and secondary storage devices 108. Storage devices can generally be of any suitable type including, without limitation, disk drives, storage arrays (e.g., storage-area network (SAN) and/or network-attached storage (NAS) technology), semiconductor memory (e.g., solid state storage devices), network attached storage (NAS) devices, tape libraries, or other magnetic, non-tape storage devices, optical media storage devices, combinations of the same, etc. In some embodiments, storage devices form part of a distributed file system. In some cases, storage devices are provided in a cloud storage environment (e.g., a private cloud or one operated by a third-party vendor), whether for primary data or secondary copies or both.
Depending on context, the term “information management system” can refer to generally all of the illustrated hardware and software components in
One or more client computing devices 102 may be part of system 100, each client computing device 102 having an operating system and at least one application 110 and one or more accompanying data agents executing thereon; and associated with one or more primary storage devices 104 storing primary data 112. Client computing device(s) 102 and primary storage devices 104 may generally be referred to in some cases as primary storage subsystem 117.
Client Computing Devices, Clients, and Subclients
Typically, a variety of sources in an organization produce data to be protected and managed. As just one illustrative example, in a corporate environment such data sources can be employee workstations and company servers such as a mail server, a web server, a database server, a transaction server, or the like. In system 100, data generation sources include one or more client computing devices 102. A computing device that has a data agent 142 installed and operating on it is generally referred to as a “client computing device” 102, and may include any type of computing device, without limitation. A client computing device 102 may be associated with one or more users and/or user accounts.
A “client” is a logical component of information management system 100, which may represent a logical grouping of one or more data agents installed on a client computing device 102. Storage manager 140 recognizes a client as a component of system 100, and in some embodiments, may automatically create a client component the first time a data agent 142 is installed on a client computing device 102. Because data generated by executable component(s) 110 is tracked by the associated data agent 142 so that it may be properly protected in system 100, a client may be said to generate data and to store the generated data to primary storage, such as primary storage device 104. However, the terms “client” and “client computing device” as used herein do not imply that a client computing device 102 is necessarily configured in the client/server sense relative to another computing device such as a mail server, or that a client computing device 102 cannot be a server in its own right. As just a few examples, a client computing device 102 can be and/or include mail servers, file servers, database servers, virtual machine servers, and/or web servers.
Each client computing device 102 may have application(s) 110 executing thereon which generate and manipulate the data that is to be protected from loss and managed in system 100. Applications 110 generally facilitate the operations of an organization, and can include, without limitation, mail server applications (e.g., Microsoft Exchange Server), file system applications, mail client applications (e.g., Microsoft Exchange Client), database applications or database management systems (e.g., SQL, Oracle, SAP, Lotus Notes Database), word processing applications (e.g., Microsoft Word), spreadsheet applications, financial applications, presentation applications, graphics and/or video applications, browser applications, mobile applications, entertainment applications, and so on. Each application 110 may be accompanied by an application-specific data agent 142, though not all data agents 142 are application-specific or associated with only application. A file manager application, e.g., Microsoft Windows Explorer, may be considered an application 110 and may be accompanied by its own data agent 142. Client computing devices 102 can have at least one operating system (e.g., Microsoft Windows, Mac OS X, iOS, IBM z/OS, Linux, other Unix-based operating systems, etc.) installed thereon, which may support or host one or more file systems and other applications 110. In some embodiments, a virtual machine that executes on a host client computing device 102 may be considered an application 110 and may be accompanied by a specific data agent 142 (e.g., virtual server data agent).
Client computing devices 102 and other components in system 100 can be connected to one another via one or more electronic communication pathways 114. For example, a first communication pathway 114 may communicatively couple client computing device 102 and secondary storage computing device 106; a second communication pathway 114 may communicatively couple storage manager 140 and client computing device 102; and a third communication pathway 114 may communicatively couple storage manager 140 and secondary storage computing device 106, etc. (see, e.g.,
A “subclient” is a logical grouping of all or part of a client's primary data 112. In general, a subclient may be defined according to how the subclient data is to be protected as a unit in system 100. For example, a subclient may be associated with a certain storage policy. A given client may thus comprise several subclients, each subclient associated with a different storage policy. For example, some files may form a first subclient that requires compression and deduplication and is associated with a first storage policy. Other files of the client may form a second subclient that requires a different retention schedule as well as encryption, and may be associated with a different, second storage policy. As a result, though the primary data may be generated by the same application 110 and may belong to one given client, portions of the data may be assigned to different subclients for distinct treatment by system 100. More detail on subclients is given in regard to storage policies below.
Primary Data and Example Primary Storage Devices
Primary data 112 is generally production data or “live” data generated by the operating system and/or applications 110 executing on client computing device 102. Primary data 112 is generally stored on primary storage device(s) 104 and is organized via a file system operating on the client computing device 102. Thus, client computing device(s) 102 and corresponding applications 110 may create, access, modify, write, delete, and otherwise use primary data 112. Primary data 112 is generally in the native format of the source application 110. Primary data 112 is an initial or first stored body of data generated by the source application 110. Primary data 112 in some cases is created substantially directly from data generated by the corresponding source application 110. It can be useful in performing certain tasks to organize primary data 112 into units of different granularities. In general, primary data 112 can include files, directories, file system volumes, data blocks, extents, or any other hierarchies or organizations of data objects. As used herein, a “data object” can refer to (i) any file that is currently addressable by a file system or that was previously addressable by the file system (e.g., an archive file), and/or to (ii) a subset of such a file (e.g., a data block, an extent, etc.). Primary data 112 may include structured data (e.g., database files), unstructured data (e.g., documents), and/or semi-structured data. See, e.g.,
It can also be useful in performing certain functions of system 100 to access and modify metadata within primary data 112. Metadata generally includes information about data objects and/or characteristics associated with the data objects. For simplicity herein, it is to be understood that, unless expressly stated otherwise, any reference to primary data 112 generally also includes its associated metadata, but references to metadata generally do not include the primary data. Metadata can include, without limitation, one or more of the following: the data owner (e.g., the client or user that generates the data), the last modified time (e.g., the time of the most recent modification of the data object), a data object name (e.g., a file name), a data object size (e.g., a number of bytes of data), information about the content (e.g., an indication as to the existence of a particular search term), user-supplied tags, to/from information for email (e.g., an email sender, recipient, etc.), creation date, file type (e.g., format or application type), last accessed time, application type (e.g., type of application that generated the data object), location/network (e.g., a current, past or future location of the data object and network pathways to/from the data object), geographic location (e.g., GPS coordinates), frequency of change (e.g., a period in which the data object is modified), business unit (e.g., a group or department that generates, manages or is otherwise associated with the data object), aging information (e.g., a schedule, such as a time period, in which the data object is migrated to secondary or long term storage), boot sectors, partition layouts, file location within a file folder directory structure, user permissions, owners, groups, access control lists (ACLs), system metadata (e.g., registry information), combinations of the same or other similar information related to the data object. In addition to metadata generated by or related to file systems and operating systems, some applications 110 and/or other components of system 100 maintain indices of metadata for data objects, e.g., metadata associated with individual email messages. The use of metadata to perform classification and other functions is described in greater detail below.
Primary storage devices 104 storing primary data 112 may be relatively fast and/or expensive technology (e.g., flash storage, a disk drive, a hard-disk storage array, solid state memory, etc.), typically to support high-performance live production environments. Primary data 112 may be highly changeable and/or may be intended for relatively short term retention (e.g., hours, days, or weeks). According to some embodiments, client computing device 102 can access primary data 112 stored in primary storage device 104 by making conventional file system calls via the operating system. Each client computing device 102 is generally associated with and/or in communication with one or more primary storage devices 104 storing corresponding primary data 112. A client computing device 102 is said to be associated with or in communication with a particular primary storage device 104 if it is capable of one or more of: routing and/or storing data (e.g., primary data 112) to the primary storage device 104, coordinating the routing and/or storing of data to the primary storage device 104, retrieving data from the primary storage device 104, coordinating the retrieval of data from the primary storage device 104, and modifying and/or deleting data in the primary storage device 104. Thus, a client computing device 102 may be said to access data stored in an associated storage device 104.
Primary storage device 104 may be dedicated or shared. In some cases, each primary storage device 104 is dedicated to an associated client computing device 102, e.g., a local disk drive. In other cases, one or more primary storage devices 104 can be shared by multiple client computing devices 102, e.g., via a local network, in a cloud storage implementation, etc. As one example, primary storage device 104 can be a storage array shared by a group of client computing devices 102, such as EMC Clariion, EMC Symmetrix, EMC Celerra, Dell EqualLogic, IBM XIV, NetApp FAS, HP EVA, and HP 3PAR.
System 100 may also include hosted services (not shown), which may be hosted in some cases by an entity other than the organization that employs the other components of system 100. For instance, the hosted services may be provided by online service providers. Such service providers can provide social networking services, hosted email services, or hosted productivity applications or other hosted applications such as software-as-a-service (SaaS), platform-as-a-service (PaaS), application service providers (ASPs), cloud services, or other mechanisms for delivering functionality via a network. As it services users, each hosted service may generate additional data and metadata, which may be managed by system 100, e.g., as primary data 112. In some cases, the hosted services may be accessed using one of the applications 110. As an example, a hosted mail service may be accessed via browser running on a client computing device 102.
Secondary Copies and Example Secondary Storage Devices
Primary data 112 stored on primary storage devices 104 may be compromised in some cases, such as when an employee deliberately or accidentally deletes or overwrites primary data 112. Or primary storage devices 104 can be damaged, lost, or otherwise corrupted. For recovery and/or regulatory compliance purposes, it is therefore useful to generate and maintain copies of primary data 112. Accordingly, system 100 includes one or more secondary storage computing devices 106 and one or more secondary storage devices 108 configured to create and store one or more secondary copies 116 of primary data 112 including its associated metadata. The secondary storage computing devices 106 and the secondary storage devices 108 may be referred to as secondary storage subsystem 118.
Secondary copies 116 can help in search and analysis efforts and meet other information management goals as well, such as: restoring data and/or metadata if an original version is lost (e.g., by deletion, corruption, or disaster); allowing point-in-time recovery; complying with regulatory data retention and electronic discovery (e-discovery) requirements; reducing utilized storage capacity in the production system and/or in secondary storage; facilitating organization and search of data; improving user access to data files across multiple computing devices and/or hosted services; and implementing data retention and pruning policies.
A secondary copy 116 can comprise a separate stored copy of data that is derived from one or more earlier-created stored copies (e.g., derived from primary data 112 or from another secondary copy 116). Secondary copies 116 can include point-in-time data, and may be intended for relatively long-term retention before some or all of the data is moved to other storage or discarded. In some cases, a secondary copy 116 may be in a different storage device than other previously stored copies; and/or may be remote from other previously stored copies. Secondary copies 116 can be stored in the same storage device as primary data 112. For example, a disk array capable of performing hardware snapshots stores primary data 112 and creates and stores hardware snapshots of the primary data 112 as secondary copies 116. Secondary copies 116 may be stored in relatively slow and/or lower cost storage (e.g., magnetic tape). A secondary copy 116 may be stored in a backup or archive format, or in some other format different from the native source application format or other format of primary data 112.
Secondary storage computing devices 106 may index secondary copies 116 (e.g., using a media agent 144), enabling users to browse and restore at a later time and further enabling the lifecycle management of the indexed data. After creation of a secondary copy 116 that represents certain primary data 112, a pointer or other location indicia (e.g., a stub) may be placed in primary data 112, or be otherwise associated with primary data 112, to indicate the current location of a particular secondary copy 116. Since an instance of a data object or metadata in primary data 112 may change over time as it is modified by application 110 (or hosted service or the operating system), system 100 may create and manage multiple secondary copies 116 of a particular data object or metadata, each copy representing the state of the data object in primary data 112 at a particular point in time. Moreover, since an instance of a data object in primary data 112 may eventually be deleted from primary storage device 104 and the file system, system 100 may continue to manage point-in-time representations of that data object, even though the instance in primary data 112 no longer exists. For virtual machines, the operating system and other applications 110 of client computing device(s) 102 may execute within or under the management of virtualization software (e.g., a VMM), and the primary storage device(s) 104 may comprise a virtual disk created on a physical storage device. System 100 may create secondary copies 116 of the files or other data objects in a virtual disk file and/or secondary copies 116 of the entire virtual disk file itself (e.g., of an entire .vmdk file).
Secondary copies 116 are distinguishable from corresponding primary data 112. First, secondary copies 116 can be stored in a different format from primary data 112 (e.g., backup, archive, or other non-native format). For this or other reasons, secondary copies 116 may not be directly usable by applications 110 or client computing device 102 (e.g., via standard system calls or otherwise) without modification, processing, or other intervention by system 100 which may be referred to as “restore” operations. Secondary copies 116 may have been processed by data agent 142 and/or media agent 144 in the course of being created (e.g., compression, deduplication, encryption, integrity markers, indexing, formatting, application-aware metadata, etc.), and thus secondary copy 116 may represent source primary data 112 without necessarily being exactly identical to the source.
Second, secondary copies 116 may be stored on a secondary storage device 108 that is inaccessible to application 110 running on client computing device 102 and/or hosted service. Some secondary copies 116 may be “offline copies,” in that they are not readily available (e.g., not mounted to tape or disk). Offline copies can include copies of data that system 100 can access without human intervention (e.g., tapes within an automated tape library, but not yet mounted in a drive), and copies that the system 100 can access only with some human intervention (e.g., tapes located at an offsite storage site).
Using Intermediate Devices for Creating Secondary Copies—Secondary Storage Computing Devices
Creating secondary copies can be challenging when hundreds or thousands of client computing devices 102 continually generate large volumes of primary data 112 to be protected. Also, there can be significant overhead involved in the creation of secondary copies 116. Moreover, specialized programmed intelligence and/or hardware capability is generally needed for accessing and interacting with secondary storage devices 108. Client computing devices 102 may interact directly with a secondary storage device 108 to create secondary copies 116, but in view of the factors described above, this approach can negatively impact the ability of client computing device 102 to serve/service application 110 and produce primary data 112. Further, any given client computing device 102 may not be optimized for interaction with certain secondary storage devices 108.
Thus, system 100 may include one or more software and/or hardware components which generally act as intermediaries between client computing devices 102 (that generate primary data 112) and secondary storage devices 108 (that store secondary copies 116). In addition to off-loading certain responsibilities from client computing devices 102, these intermediate components provide other benefits. For instance, as discussed further below with respect to
Secondary storage computing device(s) 106 can comprise any of the computing devices described above, without limitation. In some cases, secondary storage computing device(s) 106 also include specialized hardware componentry and/or software intelligence (e.g., specialized interfaces) for interacting with certain secondary storage device(s) 108 with which they may be specially associated.
To create a secondary copy 116 involving the copying of data from primary storage subsystem 117 to secondary storage subsystem 118, client computing device 102 may communicate the primary data 112 to be copied (or a processed version thereof generated by a data agent 142) to the designated secondary storage computing device 106, via a communication pathway 114. Secondary storage computing device 106 in turn may further process and convey the data or a processed version thereof to secondary storage device 108. One or more secondary copies 116 may be created from existing secondary copies 116, such as in the case of an auxiliary copy operation, described further below.
Example Primary Data and an Example Secondary Copy
Secondary copy data objects 134A-C can individually represent more than one primary data object. For example, secondary copy data object 134A represents three separate primary data objects 133C, 122, and 129C (represented as 133C′, 122′, and 129C′, respectively, and accompanied by corresponding metadata Meta11, Meta3, and Meta8, respectively). Moreover, as indicated by the prime mark (′), secondary storage computing devices 106 or other components in secondary storage subsystem 118 may process the data received from primary storage subsystem 117 and store a secondary copy including a transformed and/or supplemented representation of a primary data object and/or metadata that is different from the original format, e.g., in a compressed, encrypted, deduplicated, or other modified format. For instance, secondary storage computing devices 106 can generate new metadata or other information based on said processing, and store the newly generated information along with the secondary copies. Secondary copy data object 1346 represents primary data objects 120, 1336, and 119A as 120′, 1336′, and 119A′, respectively, accompanied by corresponding metadata Meta2, Meta10, and Meta1, respectively. Also, secondary copy data object 134C represents primary data objects 133A, 1196, and 129A as 133A′, 1196′, and 129A′, respectively, accompanied by corresponding metadata Meta9, Meta5, and Meta6, respectively.
Example Information Management System Architecture
System 100 can incorporate a variety of different hardware and software components, which can in turn be organized with respect to one another in many different configurations, depending on the embodiment. There are critical design choices involved in specifying the functional responsibilities of the components and the role of each component in system 100. Such design choices can impact how system 100 performs and adapts to data growth and other changing circumstances.
Storage Manager
Storage manager 140 is a centralized storage and/or information manager that is configured to perform certain control functions and also to store certain critical information about system 100—hence storage manager 140 is said to manage system 100. As noted, the number of components in system 100 and the amount of data under management can be large. Managing the components and data is therefore a significant task, which can grow unpredictably as the number of components and data scale to meet the needs of the organization. For these and other reasons, according to certain embodiments, responsibility for controlling system 100, or at least a significant portion of that responsibility, is allocated to storage manager 140. Storage manager 140 can be adapted independently according to changing circumstances, without having to replace or re-design the remainder of the system. Moreover, a computing device for hosting and/or operating as storage manager 140 can be selected to best suit the functions and networking needs of storage manager 140. These and other advantages are described in further detail below and with respect to
Storage manager 140 may be a software module or other application hosted by a suitable computing device. In some embodiments, storage manager 140 is itself a computing device that performs the functions described herein. Storage manager 140 comprises or operates in conjunction with one or more associated data structures such as a dedicated database (e.g., management database 146), depending on the configuration. The storage manager 140 generally initiates, performs, coordinates, and/or controls storage and other information management operations performed by system 100, e.g., to protect and control primary data 112 and secondary copies 116. In general, storage manager 140 is said to manage system 100, which includes communicating with, instructing, and controlling in some circumstances components such as data agents 142 and media agents 144, etc.
As shown by the dashed arrowed lines 114 in
According to certain embodiments, storage manager 140 provides one or more of the following functions:
Storage manager 140 may maintain an associated database 146 (or “storage manager database 146” or “management database 146”) of management-related data and information management policies 148. Database 146 is stored in computer memory accessible by storage manager 140. Database 146 may include a management index 150 (or “index 150”) or other data structure(s) that may store: logical associations between components of the system; user preferences and/or profiles (e.g., preferences regarding encryption, compression, or deduplication of primary data or secondary copies; preferences regarding the scheduling, type, or other aspects of secondary copy or other operations; mappings of particular information management users or user accounts to certain computing devices or other components, etc.; management tasks; media containerization; other useful data; and/or any combination thereof. For example, storage manager 140 may use index 150 to track logical associations between media agents 144 and secondary storage devices 108 and/or movement of data to/from secondary storage devices 108. For instance, index 150 may store data associating a client computing device 102 with a particular media agent 144 and/or secondary storage device 108, as specified in an information management policy 148.
Administrators and others may configure and initiate certain information management operations on an individual basis. But while this may be acceptable for some recovery operations or other infrequent tasks, it is often not workable for implementing on-going organization-wide data protection and management. Thus, system 100 may utilize information management policies 148 for specifying and executing information management operations on an automated basis. Generally, an information management policy 148 can include a stored data structure or other information source that specifies parameters (e.g., criteria and rules) associated with storage management or other information management operations. Storage manager 140 can process an information management policy 148 and/or index 150 and, based on the results, identify an information management operation to perform, identify the appropriate components in system 100 to be involved in the operation (e.g., client computing devices 102 and corresponding data agents 142, secondary storage computing devices 106 and corresponding media agents 144, etc.), establish connections to those components and/or between those components, and/or instruct and control those components to carry out the operation. In this manner, system 100 can translate stored information into coordinated activity among the various computing devices in system 100.
Management database 146 may maintain information management policies 148 and associated data, although information management policies 148 can be stored in computer memory at any appropriate location outside management database 146. For instance, an information management policy 148 such as a storage policy may be stored as metadata in a media agent database 152 or in a secondary storage device 108 (e.g., as an archive copy) for use in restore or other information management operations, depending on the embodiment. Information management policies 148 are described further below. According to certain embodiments, management database 146 comprises a relational database (e.g., an SQL database) for tracking metadata, such as metadata associated with secondary copy operations (e.g., what client computing devices 102 and corresponding subclient data were protected and where the secondary copies are stored and which media agent 144 performed the storage operation(s)). This and other metadata may additionally be stored in other locations, such as at secondary storage computing device 106 or on the secondary storage device 108, allowing data recovery without the use of storage manager 140 in some cases. Thus, management database 146 may comprise data needed to kick off secondary copy operations (e.g., storage policies, schedule policies, etc.), status and reporting information about completed jobs (e.g., status and error reports on yesterday's backup jobs), and additional information sufficient to enable restore and disaster recovery operations (e.g., media agent associations, location indexing, content indexing, etc.).
Storage manager 140 may include a jobs agent 156, a user interface 158, and a management agent 154, all of which may be implemented as interconnected software modules or application programs. These are described further below.
Jobs agent 156 in some embodiments initiates, controls, and/or monitors the status of some or all information management operations previously performed, currently being performed, or scheduled to be performed by system 100. A job is a logical grouping of information management operations such as daily storage operations scheduled for a certain set of subclients (e.g., generating incremental block-level backup copies 116 at a certain time every day for database files in a certain geographical location). Thus, jobs agent 156 may access information management policies 148 (e.g., in management database 146) to determine when, where, and how to initiate/control jobs in system 100.
Storage Manager User Interfaces
User interface 158 may include information processing and display software, such as a graphical user interface (GUI), an application program interface (API), and/or other interactive interface(s) through which users and system processes can retrieve information about the status of information management operations or issue instructions to storage manager 140 and other components. Via user interface 158, users may issue instructions to the components in system 100 regarding performance of secondary copy and recovery operations. For example, a user may modify a schedule concerning the number of pending secondary copy operations. As another example, a user may employ the GUI to view the status of pending secondary copy jobs or to monitor the status of certain components in system 100 (e.g., the amount of capacity left in a storage device). Storage manager 140 may track information that permits it to select, designate, or otherwise identify content indices, deduplication databases, or similar databases or resources or data sets within its information management cell (or another cell) to be searched in response to certain queries. Such queries may be entered by the user by interacting with user interface 158.
Various embodiments of information management system 100 may be configured and/or designed to generate user interface data usable for rendering the various interactive user interfaces described. The user interface data may be used by system 100 and/or by another system, device, and/or software program (for example, a browser program), to render the interactive user interfaces. The interactive user interfaces may be displayed on, for example, electronic displays (including, for example, touch-enabled displays), consoles, etc., whether direct-connected to storage manager 140 or communicatively coupled remotely, e.g., via an internet connection. The present disclosure describes various embodiments of interactive and dynamic user interfaces, some of which may be generated by user interface agent 158, and which are the result of significant technological development. The user interfaces described herein may provide improved human-computer interactions, allowing for significant cognitive and ergonomic efficiencies and advantages over previous systems, including reduced mental workloads, improved decision-making, and the like. User interface 158 may operate in a single integrated view or console (not shown). The console may support a reporting capability for generating a variety of reports, which may be tailored to a particular aspect of information management.
User interfaces are not exclusive to storage manager 140 and in some embodiments a user may access information locally from a computing device component of system 100. For example, some information pertaining to installed data agents 142 and associated data streams may be available from client computing device 102. Likewise, some information pertaining to media agents 144 and associated data streams may be available from secondary storage computing device 106.
Storage Manager Management Agent
Management agent 154 can provide storage manager 140 with the ability to communicate with other components within system 100 and/or with other information management cells via network protocols and application programming interfaces (APIs) including, e.g., HTTP, HTTPS, FTP, REST, virtualization software APIs, cloud service provider APIs, and hosted service provider APIs, without limitation. Management agent 154 also allows multiple information management cells to communicate with one another. For example, system 100 in some cases may be one information management cell in a network of multiple cells adjacent to one another or otherwise logically related, e.g., in a WAN or LAN. With this arrangement, the cells may communicate with one another through respective management agents 154. Inter-cell communications and hierarchy is described in greater detail in e.g., U.S. Pat. No. 7,343,453.
Information Management Cell
An “information management cell” (or “storage operation cell” or “cell”) may generally include a logical and/or physical grouping of a combination of hardware and software components associated with performing information management operations on electronic data, typically one storage manager 140 and at least one data agent 142 (executing on a client computing device 102) and at least one media agent 144 (executing on a secondary storage computing device 106). For instance, the components shown in
Multiple cells may be organized hierarchically, so that cells may inherit properties from hierarchically superior cells or be controlled by other cells in the hierarchy (automatically or otherwise). Alternatively, in some embodiments, cells may inherit or otherwise be associated with information management policies, preferences, information management operational parameters, or other properties or characteristics according to their relative position in a hierarchy of cells. Cells may also be organized hierarchically according to function, geography, architectural considerations, or other factors useful or desirable in performing information management operations. For example, a first cell may represent a geographic segment of an enterprise, such as a Chicago office, and a second cell may represent a different geographic segment, such as a New York City office. Other cells may represent departments within a particular office, e.g., human resources, finance, engineering, etc. Where delineated by function, a first cell may perform one or more first types of information management operations (e.g., one or more first types of secondary copies at a certain frequency), and a second cell may perform one or more second types of information management operations (e.g., one or more second types of secondary copies at a different frequency and under different retention rules). In general, the hierarchical information is maintained by one or more storage managers 140 that manage the respective cells (e.g., in corresponding management database(s) 146).
Data Agents
A variety of different applications 110 can operate on a given client computing device 102, including operating systems, file systems, database applications, e-mail applications, and virtual machines, just to name a few. And, as part of the process of creating and restoring secondary copies 116, the client computing device 102 may be tasked with processing and preparing the primary data 112 generated by these various applications 110. Moreover, the nature of the processing/preparation can differ across application types, e.g., due to inherent structural, state, and formatting differences among applications 110 and/or the operating system of client computing device 102. Each data agent 142 is therefore advantageously configured in some embodiments to assist in the performance of information management operations based on the type of data that is being protected at a client-specific and/or application-specific level.
Data agent 142 is a component of information system 100 and is generally directed by storage manager 140 to participate in creating or restoring secondary copies 116. Data agent 142 may be a software program (e.g., in the form of a set of executable binary files) that executes on the same client computing device 102 as the associated application 110 that data agent 142 is configured to protect. Data agent 142 is generally responsible for managing, initiating, or otherwise assisting in the performance of information management operations in reference to its associated application(s) 110 and corresponding primary data 112 which is generated/accessed by the particular application(s) 110. For instance, data agent 142 may take part in copying, archiving, migrating, and/or replicating of certain primary data 112 stored in the primary storage device(s) 104. Data agent 142 may receive control information from storage manager 140, such as commands to transfer copies of data objects and/or metadata to one or more media agents 144. Data agent 142 also may compress, deduplicate, and encrypt certain primary data 112, as well as capture application-related metadata before transmitting the processed data to media agent 144. Data agent 142 also may receive instructions from storage manager 140 to restore (or assist in restoring) a secondary copy 116 from secondary storage device 108 to primary storage 104, such that the restored data may be properly accessed by application 110 in a suitable format as though it were primary data 112.
Each data agent 142 may be specialized for a particular application 110. For instance, different individual data agents 142 may be designed to handle Microsoft Exchange data, Lotus Notes data, Microsoft Windows file system data, Microsoft Active Directory Objects data, SQL Server data, SharePoint data, Oracle database data, SAP database data, virtual machines and/or associated data, and other types of data. A file system data agent, for example, may handle data files and/or other file system information. If a client computing device 102 has two or more types of data 112, a specialized data agent 142 may be used for each data type. For example, to backup, migrate, and/or restore all of the data on a Microsoft Exchange server, the client computing device 102 may use: (1) a Microsoft Exchange Mailbox data agent 142 to back up the Exchange mailboxes; (2) a Microsoft Exchange Database data agent 142 to back up the Exchange databases; (3) a Microsoft Exchange Public Folder data agent 142 to back up the Exchange Public Folders; and (4) a Microsoft Windows File System data agent 142 to back up the file system of client computing device 102. In this example, these specialized data agents 142 are treated as four separate data agents 142 even though they operate on the same client computing device 102. Other examples may include archive management data agents such as a migration archiver or a compliance archiver, Quick Recovery® agents, and continuous data replication agents. Application-specific data agents 142 can provide improved performance as compared to generic agents. For instance, because application-specific data agents 142 may only handle data for a single software application, the design, operation, and performance of the data agent 142 can be streamlined. The data agent 142 may therefore execute faster and consume less persistent storage and/or operating memory than data agents designed to generically accommodate multiple different software applications 110.
Each data agent 142 may be configured to access data and/or metadata stored in the primary storage device(s) 104 associated with data agent 142 and its host client computing device 102, and process the data appropriately. For example, during a secondary copy operation, data agent 142 may arrange or assemble the data and metadata into one or more files having a certain format (e.g., a particular backup or archive format) before transferring the file(s) to a media agent 144 or other component. The file(s) may include a list of files or other metadata. In some embodiments, a data agent 142 may be distributed between client computing device 102 and storage manager 140 (and any other intermediate components) or may be deployed from a remote location or its functions approximated by a remote process that performs some or all of the functions of data agent 142. In addition, a data agent 142 may perform some functions provided by media agent 144. Other embodiments may employ one or more generic data agents 142 that can handle and process data from two or more different applications 110, or that can handle and process multiple data types, instead of or in addition to using specialized data agents 142. For example, one generic data agent 142 may be used to back up, migrate and restore Microsoft Exchange Mailbox data and Microsoft Exchange Database data, while another generic data agent may handle Microsoft Exchange Public Folder data and Microsoft Windows File System data.
Media Agents
As noted, off-loading certain responsibilities from client computing devices 102 to intermediate components such as secondary storage computing device(s) 106 and corresponding media agent(s) 144 can provide a number of benefits including improved performance of client computing device 102, faster and more reliable information management operations, and enhanced scalability. In one example which will be discussed further below, media agent 144 can act as a local cache of recently-copied data and/or metadata stored to secondary storage device(s) 108, thus improving restore capabilities and performance for the cached data.
Media agent 144 is a component of system 100 and is generally directed by storage manager 140 in creating and restoring secondary copies 116. Whereas storage manager 140 generally manages system 100 as a whole, media agent 144 provides a portal to certain secondary storage devices 108, such as by having specialized features for communicating with and accessing certain associated secondary storage device 108. Media agent 144 may be a software program (e.g., in the form of a set of executable binary files) that executes on a secondary storage computing device 106. Media agent 144 generally manages, coordinates, and facilitates the transmission of data between a data agent 142 (executing on client computing device 102) and secondary storage device(s) 108 associated with media agent 144. For instance, other components in the system may interact with media agent 144 to gain access to data stored on associated secondary storage device(s) 108, (e.g., to browse, read, write, modify, delete, or restore data). Moreover, media agents 144 can generate and store information relating to characteristics of the stored data and/or metadata, or can generate and store other types of information that generally provides insight into the contents of the secondary storage devices 108—generally referred to as indexing of the stored secondary copies 116. Each media agent 144 may operate on a dedicated secondary storage computing device 106, while in other embodiments a plurality of media agents 144 may operate on the same secondary storage computing device 106.
A media agent 144 may be associated with a particular secondary storage device 108 if that media agent 144 is capable of one or more of: routing and/or storing data to the particular secondary storage device 108; coordinating the routing and/or storing of data to the particular secondary storage device 108; retrieving data from the particular secondary storage device 108; coordinating the retrieval of data from the particular secondary storage device 108; and modifying and/or deleting data retrieved from the particular secondary storage device 108. Media agent 144 in certain embodiments is physically separate from the associated secondary storage device 108. For instance, a media agent 144 may operate on a secondary storage computing device 106 in a distinct housing, package, and/or location from the associated secondary storage device 108. In one example, a media agent 144 operates on a first server computer and is in communication with a secondary storage device(s) 108 operating in a separate rack-mounted RAID-based system.
A media agent 144 associated with a particular secondary storage device 108 may instruct secondary storage device 108 to perform an information management task. For instance, a media agent 144 may instruct a tape library to use a robotic arm or other retrieval means to load or eject a certain storage media, and to subsequently archive, migrate, or retrieve data to or from that media, e.g., for the purpose of restoring data to a client computing device 102. As another example, a secondary storage device 108 may include an array of hard disk drives or solid state drives organized in a RAID configuration, and media agent 144 may forward a logical unit number (LUN) and other appropriate information to the array, which uses the received information to execute the desired secondary copy operation. Media agent 144 may communicate with a secondary storage device 108 via a suitable communications link, such as a SCSI or Fibre Channel link.
Each media agent 144 may maintain an associated media agent database 152. Media agent database 152 may be stored to a disk or other storage device (not shown) that is local to the secondary storage computing device 106 on which media agent 144 executes. In other cases, media agent database 152 is stored separately from the host secondary storage computing device 106. Media agent database 152 can include, among other things, a media agent index 153 (see, e.g.,
Media agent index 153 (or “index 153”) may be a data structure associated with the particular media agent 144 that includes information about the stored data associated with the particular media agent and which may be generated in the course of performing a secondary copy operation or a restore. Index 153 provides a fast and efficient mechanism for locating/browsing secondary copies 116 or other data stored in secondary storage devices 108 without having to access secondary storage device 108 to retrieve the information from there. For instance, for each secondary copy 116, index 153 may include metadata such as a list of the data objects (e.g., files/subdirectories, database objects, mailbox objects, etc.), a logical path to the secondary copy 116 on the corresponding secondary storage device 108, location information (e.g., offsets) indicating where the data objects are stored in the secondary storage device 108, when the data objects were created or modified, etc. Thus, index 153 includes metadata associated with the secondary copies 116 that is readily available for use from media agent 144. In some embodiments, some or all of the information in index 153 may instead or additionally be stored along with secondary copies 116 in secondary storage device 108. In some embodiments, a secondary storage device 108 can include sufficient information to enable a “bare metal restore,” where the operating system and/or software applications of a failed client computing device 102 or another target may be automatically restored without manually reinstalling individual software packages (including operating systems).
Because index 153 may operate as a cache, it can also be referred to as an “index cache.” In such cases, information stored in index cache 153 typically comprises data that reflects certain particulars about relatively recent secondary copy operations. After some triggering event, such as after some time elapses or index cache 153 reaches a particular size, certain portions of index cache 153 may be copied or migrated to secondary storage device 108, e.g., on a least-recently-used basis. This information may be retrieved and uploaded back into index cache 153 or otherwise restored to media agent 144 to facilitate retrieval of data from the secondary storage device(s) 108. In some embodiments, the cached information may include format or containerization information related to archives or other files stored on storage device(s) 108.
In some alternative embodiments media agent 144 generally acts as a coordinator or facilitator of secondary copy operations between client computing devices 102 and secondary storage devices 108, but does not actually write the data to secondary storage device 108. For instance, storage manager 140 (or media agent 144) may instruct a client computing device 102 and secondary storage device 108 to communicate with one another directly. In such a case, client computing device 102 transmits data directly or via one or more intermediary components to secondary storage device 108 according to the received instructions, and vice versa. Media agent 144 may still receive, process, and/or maintain metadata related to the secondary copy operations, i.e., may continue to build and maintain index 153. In these embodiments, payload data can flow through media agent 144 for the purposes of populating index 153, but not for writing to secondary storage device 108. Media agent 144 and/or other components such as storage manager 140 may in some cases incorporate additional functionality, such as data classification, content indexing, deduplication, encryption, compression, and the like. Further details regarding these and other functions are described below.
Distributed, Scalable Architecture
As described, certain functions of system 100 can be distributed amongst various physical and/or logical components. For instance, one or more of storage manager 140, data agents 142, and media agents 144 may operate on computing devices that are physically separate from one another. This architecture can provide a number of benefits. For instance, hardware and software design choices for each distributed component can be targeted to suit its particular function. The secondary computing devices 106 on which media agents 144 operate can be tailored for interaction with associated secondary storage devices 108 and provide fast index cache operation, among other specific tasks. Similarly, client computing device(s) 102 can be selected to effectively service applications 110 in order to efficiently produce and store primary data 112.
Moreover, in some cases, one or more of the individual components of information management system 100 can be distributed to multiple separate computing devices. As one example, for large file systems where the amount of data stored in management database 146 is relatively large, database 146 may be migrated to or may otherwise reside on a specialized database server (e.g., an SQL server) separate from a server that implements the other functions of storage manager 140. This distributed configuration can provide added protection because database 146 can be protected with standard database utilities (e.g., SQL log shipping or database replication) independent from other functions of storage manager 140. Database 146 can be efficiently replicated to a remote site for use in the event of a disaster or other data loss at the primary site. Or database 146 can be replicated to another computing device within the same site, such as to a higher performance machine in the event that a storage manager host computing device can no longer service the needs of a growing system 100.
The distributed architecture also provides scalability and efficient component utilization.
Where system 100 includes multiple media agents 144 (see, e.g.,
While distributing functionality amongst multiple computing devices can have certain advantages, in other contexts it can be beneficial to consolidate functionality on the same computing device. In alternative configurations, certain components may reside and execute on the same computing device. As such, in other embodiments, one or more of the components shown in
Example Types of Information Management Operations, Including Storage Operations
In order to protect and leverage stored data, system 100 can be configured to perform a variety of information management operations, which may also be referred to in some cases as storage management operations or storage operations. These operations can generally include (i) data movement operations, (ii) processing and data manipulation operations, and (iii) analysis, reporting, and management operations.
Data Movement Operations, Including Secondary Copy Operations
Data movement operations are generally storage operations that involve the copying or migration of data between different locations in system 100. For example, data movement operations can include operations in which stored data is copied, migrated, or otherwise transferred from one or more first storage devices to one or more second storage devices, such as from primary storage device(s) 104 to secondary storage device(s) 108, from secondary storage device(s) 108 to different secondary storage device(s) 108, from secondary storage devices 108 to primary storage devices 104, or from primary storage device(s) 104 to different primary storage device(s) 104, or in some cases within the same primary storage device 104 such as within a storage array.
Data movement operations can include by way of example, backup operations, archive operations, information lifecycle management operations such as hierarchical storage management operations, replication operations (e.g., continuous data replication), snapshot operations, deduplication or single-instancing operations, auxiliary copy operations, disaster-recovery copy operations, and the like. As will be discussed, some of these operations do not necessarily create distinct copies. Nonetheless, some or all of these operations are generally referred to as “secondary copy operations” for simplicity because they involve secondary copies. Data movement also comprises restoring secondary copies.
Backup Operations
A backup operation creates a copy of a version of primary data 112 at a particular point in time (e.g., one or more files or other data units). Each subsequent backup copy 116 (which is a form of secondary copy 116) may be maintained independently of the first. A backup generally involves maintaining a version of the copied primary data 112 as well as backup copies 116. Further, a backup copy in some embodiments is generally stored in a form that is different from the native format, e.g., a backup format. This contrasts to the version in primary data 112 which may instead be stored in a format native to the source application(s) 110. In various cases, backup copies can be stored in a format in which the data is compressed, encrypted, deduplicated, and/or otherwise modified from the original native application format. For example, a backup copy may be stored in a compressed backup format that facilitates efficient long-term storage. Backup copies 116 can have relatively long retention periods as compared to primary data 112, which is generally highly changeable. Backup copies 116 may be stored on media with slower retrieval times than primary storage device 104. Some backup copies may have shorter retention periods than some other types of secondary copies 116, such as archive copies (described below). Backups may be stored at an offsite location.
Backup operations can include full backups, differential backups, incremental backups, “synthetic full” backups, and/or creating a “reference copy.” A full backup (or “standard full backup”) in some embodiments is generally a complete image of the data to be protected. However, because full backup copies can consume a relatively large amount of storage, it can be useful to use a full backup copy as a baseline and only store changes relative to the full backup copy afterwards.
A differential backup operation (or cumulative incremental backup operation) tracks and stores changes that occurred since the last full backup. Differential backups can grow quickly in size, but can restore relatively efficiently because a restore can be completed in some cases using only the full backup copy and the latest differential copy.
An incremental backup operation generally tracks and stores changes since the most recent backup copy of any type, which can greatly reduce storage utilization. In some cases, however, restoring can be lengthy compared to full or differential backups because completing a restore operation may involve accessing a full backup in addition to multiple incremental backups.
Synthetic full backups generally consolidate data without directly backing up data from the client computing device. A synthetic full backup is created from the most recent full backup (i.e., standard or synthetic) and subsequent incremental and/or differential backups. The resulting synthetic full backup is identical to what would have been created had the last backup for the subclient been a standard full backup. Unlike standard full, incremental, and differential backups, however, a synthetic full backup does not actually transfer data from primary storage to the backup media, because it operates as a backup consolidator. A synthetic full backup extracts the index data of each participating subclient. Using this index data and the previously backed up user data images, it builds new full backup images (e.g., bitmaps), one for each subclient. The new backup images consolidate the index and user data stored in the related incremental, differential, and previous full backups into a synthetic backup file that fully represents the subclient (e.g., via pointers) but does not comprise all its constituent data.
Any of the above types of backup operations can be at the volume level, file level, or block level. Volume level backup operations generally involve copying of a data volume (e.g., a logical disk or partition) as a whole. In a file-level backup, information management system 100 generally tracks changes to individual files and includes copies of files in the backup copy. For block-level backups, files are broken into constituent blocks, and changes are tracked at the block level. Upon restore, system 100 reassembles the blocks into files in a transparent fashion. Far less data may actually be transferred and copied to secondary storage devices 108 during a file-level copy than a volume-level copy. Likewise, a block-level copy may transfer less data than a file-level copy, resulting in faster execution. However, restoring a relatively higher-granularity copy can result in longer restore times. For instance, when restoring a block-level copy, the process of locating and retrieving constituent blocks can sometimes take longer than restoring file-level backups.
A reference copy may comprise copy(ies) of selected objects from backed up data, typically to help organize data by keeping contextual information from multiple sources together, and/or help retain specific data for a longer period of time, such as for legal hold needs. A reference copy generally maintains data integrity, and when the data is restored, it may be viewed in the same format as the source data. In some embodiments, a reference copy is based on a specialized client, individual subclient and associated information management policies (e.g., storage policy, retention policy, etc.) that are administered within system 100.
Archive Operations
Because backup operations generally involve maintaining a version of the copied primary data 112 and also maintaining backup copies in secondary storage device(s) 108, they can consume significant storage capacity. To reduce storage consumption, an archive operation according to certain embodiments creates an archive copy 116 by both copying and removing source data. Or, seen another way, archive operations can involve moving some or all of the source data to the archive destination. Thus, data satisfying criteria for removal (e.g., data of a threshold age or size) may be removed from source storage. The source data may be primary data 112 or a secondary copy 116, depending on the situation. As with backup copies, archive copies can be stored in a format in which the data is compressed, encrypted, deduplicated, and/or otherwise modified from the format of the original application or source copy. In addition, archive copies may be retained for relatively long periods of time (e.g., years) and, in some cases are never deleted. In certain embodiments, archive copies may be made and kept for extended periods in order to meet compliance regulations.
Archiving can also serve the purpose of freeing up space in primary storage device(s) 104 and easing the demand on computational resources on client computing device 102. Similarly, when a secondary copy 116 is archived, the archive copy can therefore serve the purpose of freeing up space in the source secondary storage device(s) 108. Examples of data archiving operations are provided in U.S. Pat. No. 7,107,298.
Snapshot Operations
Snapshot operations can provide a relatively lightweight, efficient mechanism for protecting data. From an end-user viewpoint, a snapshot may be thought of as an “instant” image of primary data 112 at a given point in time, and may include state and/or status information relative to an application 110 that creates/manages primary data 112. In one embodiment, a snapshot may generally capture the directory structure of an object in primary data 112 such as a file or volume or other data set at a particular moment in time and may also preserve file attributes and contents. A snapshot in some cases is created relatively quickly, e.g., substantially instantly, using a minimum amount of file space, but may still function as a conventional file system backup.
A “hardware snapshot” (or “hardware-based snapshot”) operation occurs where a target storage device (e.g., a primary storage device 104 or a secondary storage device 108) performs the snapshot operation in a self-contained fashion, substantially independently, using hardware, firmware and/or software operating on the storage device itself. For instance, the storage device may perform snapshot operations generally without intervention or oversight from any of the other components of the system 100, e.g., a storage array may generate an “array-created” hardware snapshot and may also manage its storage, integrity, versioning, etc. In this manner, hardware snapshots can off-load other components of system 100 from snapshot processing. An array may receive a request from another component to take a snapshot and then proceed to execute the “hardware snapshot” operations autonomously, preferably reporting success to the requesting component.
A “software snapshot” (or “software-based snapshot”) operation, on the other hand, occurs where a component in system 100 (e.g., client computing device 102, etc.) implements a software layer that manages the snapshot operation via interaction with the target storage device. For instance, the component executing the snapshot management software layer may derive a set of pointers and/or data that represents the snapshot. The snapshot management software layer may then transmit the same to the target storage device, along with appropriate instructions for writing the snapshot. One example of a software snapshot product is Microsoft Volume Snapshot Service (VSS), which is part of the Microsoft Windows operating system.
Some types of snapshots do not actually create another physical copy of all the data as it existed at the particular point in time, but may simply create pointers that map files and directories to specific memory locations (e.g., to specific disk blocks) where the data resides as it existed at the particular point in time. For example, a snapshot copy may include a set of pointers derived from the file system or from an application. In some other cases, the snapshot may be created at the block-level, such that creation of the snapshot occurs without awareness of the file system. Each pointer points to a respective stored data block, so that collectively, the set of pointers reflect the storage location and state of the data object (e.g., file(s) or volume(s) or data set(s)) at the point in time when the snapshot copy was created.
An initial snapshot may use only a small amount of disk space needed to record a mapping or other data structure representing or otherwise tracking the blocks that correspond to the current state of the file system. Additional disk space is usually required only when files and directories change later on. Furthermore, when files change, typically only the pointers which map to blocks are copied, not the blocks themselves. For example for “copy-on-write” snapshots, when a block changes in primary storage, the block is copied to secondary storage or cached in primary storage before the block is overwritten in primary storage, and the pointer to that block is changed to reflect the new location of that block. The snapshot mapping of file system data may also be updated to reflect the changed block(s) at that particular point in time. In some other cases, a snapshot includes a full physical copy of all or substantially all of the data represented by the snapshot. Further examples of snapshot operations are provided in U.S. Pat. No. 7,529,782. A snapshot copy in many cases can be made quickly and without significantly impacting primary computing resources because large amounts of data need not be copied or moved. In some embodiments, a snapshot may exist as a virtual file system, parallel to the actual file system. Users in some cases gain read-only access to the record of files and directories of the snapshot. By electing to restore primary data 112 from a snapshot taken at a given point in time, users may also return the current file system to the state of the file system that existed when the snapshot was taken.
Replication Operations
Replication is another type of secondary copy operation. Some types of secondary copies 116 periodically capture images of primary data 112 at particular points in time (e.g., backups, archives, and snapshots). However, it can also be useful for recovery purposes to protect primary data 112 in a more continuous fashion, by replicating primary data 112 substantially as changes occur. In some cases a replication copy can be a mirror copy, for instance, where changes made to primary data 112 are mirrored or substantially immediately copied to another location (e.g., to secondary storage device(s) 108). By copying each write operation to the replication copy, two storage systems are kept synchronized or substantially synchronized so that they are virtually identical at approximately the same time. Where entire disk volumes are mirrored, however, mirroring can require significant amount of storage space and utilizes a large amount of processing resources.
According to some embodiments, secondary copy operations are performed on replicated data that represents a recoverable state, or “known good state” of a particular application running on the source system. For instance, in certain embodiments, known good replication copies may be viewed as copies of primary data 112. This feature allows the system to directly access, copy, restore, back up, or otherwise manipulate the replication copies as if they were the “live” primary data 112. This can reduce access time, storage utilization, and impact on source applications 110, among other benefits. Based on known good state information, system 100 can replicate sections of application data that represent a recoverable state rather than rote copying of blocks of data. Examples of replication operations (e.g., continuous data replication) are provided in U.S. Pat. No. 7,617,262.
Deduplication/Single-Instancing Operations
Deduplication or single-instance storage is useful to reduce the amount of non-primary data. For instance, some or all of the above-described secondary copy operations can involve deduplication in some fashion. New data is read, broken down into data portions of a selected granularity (e.g., sub-file level blocks, files, etc.), compared with corresponding portions that are already in secondary storage, and only new/changed portions are stored. Portions that already exist are represented as pointers to the already-stored data. Thus, a deduplicated secondary copy 116 may comprise actual data portions copied from primary data 112 and may further comprise pointers to already-stored data, which is generally more storage-efficient than a full copy.
In order to streamline the comparison process, system 100 may calculate and/or store signatures (e.g., hashes or cryptographically unique IDs) corresponding to the individual source data portions and compare the signatures to already-stored data signatures, instead of comparing entire data portions. In some cases, only a single instance of each data portion is stored, and deduplication operations may therefore be referred to interchangeably as “single-instancing” operations. Depending on the implementation, however, deduplication operations can store more than one instance of certain data portions, yet still significantly reduce stored-data redundancy. Depending on the embodiment, deduplication portions such as data blocks can be of fixed or variable length. Using variable length blocks can enhance deduplication by responding to changes in the data stream, but can involve more complex processing. In some cases, system 100 utilizes a technique for dynamically aligning deduplication blocks based on changing content in the data stream, as described in U.S. Pat. No. 8,364,652.
System 100 can deduplicate in a variety of manners at a variety of locations. For instance, in some embodiments, system 100 implements “target-side” deduplication by deduplicating data at the media agent 144 after being received from data agent 142. In some such cases, media agents 144 are generally configured to manage the deduplication process. For instance, one or more of the media agents 144 maintain a corresponding deduplication database that stores deduplication information (e.g., data block signatures). Examples of such a configuration are provided in U.S. Pat. No. 9,020,900. Instead of or in combination with “target-side” deduplication, “source-side” (or “client-side”) deduplication can also be performed, e.g., to reduce the amount of data to be transmitted by data agent 142 to media agent 144. Storage manager 140 may communicate with other components within system 100 via network protocols and cloud service provider APIs to facilitate cloud-based deduplication/single instancing, as exemplified in U.S. Pat. No. 8,954,446. Some other deduplication/single instancing techniques are described in U.S. Patent Pub. No. 2006/0224846 and in U.S. Pat. No. 9,098,495.
Information Lifecycle Management and Hierarchical Storage Management
In some embodiments, files and other data over their lifetime move from more expensive quick-access storage to less expensive slower-access storage. Operations associated with moving data through various tiers of storage are sometimes referred to as information lifecycle management (ILM) operations.
One type of ILM operation is a hierarchical storage management (HSM) operation, which generally automatically moves data between classes of storage devices, such as from high-cost to low-cost storage devices. For instance, an HSM operation may involve movement of data from primary storage devices 104 to secondary storage devices 108, or between tiers of secondary storage devices 108. With each tier, the storage devices may be progressively cheaper, have relatively slower access/restore times, etc. For example, movement of data between tiers may occur as data becomes less important over time. In some embodiments, an HSM operation is similar to archiving in that creating an HSM copy may (though not always) involve deleting some of the source data, e.g., according to one or more criteria related to the source data. For example, an HSM copy may include primary data 112 or a secondary copy 116 that exceeds a given size threshold or a given age threshold. Often, and unlike some types of archive copies, HSM data that is removed or aged from the source is replaced by a logical reference pointer or stub. The reference pointer or stub can be stored in the primary storage device 104 or other source storage device, such as a secondary storage device 108 to replace the deleted source data and to point to or otherwise indicate the new location in (another) secondary storage device 108.
For example, files are generally moved between higher and lower cost storage depending on how often the files are accessed. When a user requests access to HSM data that has been removed or migrated, system 100 uses the stub to locate the data and can make recovery of the data appear transparent, even though the HSM data may be stored at a location different from other source data. In this manner, the data appears to the user (e.g., in file system browsing windows and the like) as if it still resides in the source location (e.g., in a primary storage device 104). The stub may include metadata associated with the corresponding data, so that a file system and/or application can provide some information about the data object and/or a limited-functionality version (e.g., a preview) of the data object.
An HSM copy may be stored in a format other than the native application format (e.g., compressed, encrypted, deduplicated, and/or otherwise modified). In some cases, copies which involve the removal of data from source storage and the maintenance of stub or other logical reference information on source storage may be referred to generally as “online archive copies.” On the other hand, copies which involve the removal of data from source storage without the maintenance of stub or other logical reference information on source storage may be referred to as “off-line archive copies.” Examples of HSM and ILM techniques are provided in U.S. Pat. No. 7,343,453.
Auxiliary Copy Operations
An auxiliary copy is generally a copy of an existing secondary copy 116. For instance, an initial secondary copy 116 may be derived from primary data 112 or from data residing in secondary storage subsystem 118, whereas an auxiliary copy is generated from the initial secondary copy 116. Auxiliary copies provide additional standby copies of data and may reside on different secondary storage devices 108 than the initial secondary copies 116. Thus, auxiliary copies can be used for recovery purposes if initial secondary copies 116 become unavailable. Example auxiliary copy techniques are described in further detail in U.S. Pat. No. 8,230,195.
Disaster-Recovery Copy Operations
System 100 may also make and retain disaster recovery copies, often as secondary, high-availability disk copies. System 100 may create secondary copies and store them at disaster recovery locations using auxiliary copy or replication operations, such as continuous data replication technologies. Depending on the particular data protection goals, disaster recovery locations can be remote from the client computing devices 102 and primary storage devices 104, remote from some or all of the secondary storage devices 108, or both.
Data Manipulation, Including Encryption and Compression
Data manipulation and processing may include encryption and compression as well as integrity marking and checking, formatting for transmission, formatting for storage, etc. Data may be manipulated “client-side” by data agent 142 as well as “target-side” by media agent 144 in the course of creating secondary copy 116, or conversely in the course of restoring data from secondary to primary.
Encryption Operations
System 100 in some cases is configured to process data (e.g., files or other data objects, primary data 112, secondary copies 116, etc.), according to an appropriate encryption algorithm (e.g., Blowfish, Advanced Encryption Standard (AES), Triple Data Encryption Standard (3-DES), etc.) to limit access and provide data security. System 100 in some cases encrypts the data at the client level, such that client computing devices 102 (e.g., data agents 142) encrypt the data prior to transferring it to other components, e.g., before sending the data to media agents 144 during a secondary copy operation. In such cases, client computing device 102 may maintain or have access to an encryption key or passphrase for decrypting the data upon restore. Encryption can also occur when media agent 144 creates auxiliary copies or archive copies. Encryption may be applied in creating a secondary copy 116 of a previously unencrypted secondary copy 116, without limitation. In further embodiments, secondary storage devices 108 can implement built-in, high performance hardware-based encryption.
Compression Operations
Similar to encryption, system 100 may also or alternatively compress data in the course of generating a secondary copy 116. Compression encodes information such that fewer bits are needed to represent the information as compared to the original representation. Compression techniques are well known in the art. Compression operations may apply one or more data compression algorithms. Compression may be applied in creating a secondary copy 116 of a previously uncompressed secondary copy, e.g., when making archive copies or disaster recovery copies. The use of compression may result in metadata that specifies the nature of the compression, so that data may be uncompressed on restore if appropriate.
Data Analysis, Reporting, and Management Operations
Data analysis, reporting, and management operations can differ from data movement operations in that they do not necessarily involve copying, migration or other transfer of data between different locations in the system. For instance, data analysis operations may involve processing (e.g., offline processing) or modification of already stored primary data 112 and/or secondary copies 116. However, in some embodiments data analysis operations are performed in conjunction with data movement operations. Some data analysis operations include content indexing operations and classification operations which can be useful in leveraging data under management to enhance search and other features.
Classification Operations/Content Indexing
In some embodiments, information management system 100 analyzes and indexes characteristics, content, and metadata associated with primary data 112 (“online content indexing”) and/or secondary copies 116 (“off-line content indexing”). Content indexing can identify files or other data objects based on content (e.g., user-defined keywords or phrases, other keywords/phrases that are not defined by a user, etc.), and/or metadata (e.g., email metadata such as “to,” “from,” “cc,” “bcc,” attachment name, received time, etc.). Content indexes may be searched and search results may be restored.
System 100 generally organizes and catalogues the results into a content index, which may be stored within media agent database 152, for example. The content index can also include the storage locations of or pointer references to indexed data in primary data 112 and/or secondary copies 116. Results may also be stored elsewhere in system 100 (e.g., in primary storage device 104 or in secondary storage device 108). Such content index data provides storage manager 140 or other components with an efficient mechanism for locating primary data 112 and/or secondary copies 116 of data objects that match particular criteria, thus greatly increasing the search speed capability of system 100. For instance, search criteria can be specified by a user through user interface 158 of storage manager 140. Moreover, when system 100 analyzes data and/or metadata in secondary copies 116 to create an “off-line content index,” this operation has no significant impact on the performance of client computing devices 102 and thus does not take a toll on the production environment. Examples of content indexing techniques are provided in U.S. Pat. No. 8,170,995.
One or more components, such as a content index engine, can be configured to scan data and/or associated metadata for classification purposes to populate a database (or other data structure) of information, which can be referred to as a “data classification database” or a “metabase.” Depending on the embodiment, the data classification database(s) can be organized in a variety of different ways, including centralization, logical sub-divisions, and/or physical sub-divisions. For instance, one or more data classification databases may be associated with different subsystems or tiers within system 100. As an example, there may be a first metabase associated with primary storage subsystem 117 and a second metabase associated with secondary storage subsystem 118. In other cases, metabase(s) may be associated with individual components, e.g., client computing devices 102 and/or media agents 144. In some embodiments, a data classification database may reside as one or more data structures within management database 146, may be otherwise associated with storage manager 140, and/or may reside as a separate component. In some cases, metabase(s) may be included in separate database(s) and/or on separate storage device(s) from primary data 112 and/or secondary copies 116, such that operations related to the metabase(s) do not significantly impact performance on other components of system 100. In other cases, metabase(s) may be stored along with primary data 112 and/or secondary copies 116. Files or other data objects can be associated with identifiers (e.g., tag entries, etc.) to facilitate searches of stored data objects. Among a number of other benefits, the metabase can also allow efficient, automatic identification of files or other data objects to associate with secondary copy or other information management operations. For instance, a metabase can dramatically improve the speed with which system 100 can search through and identify data as compared to other approaches that involve scanning an entire file system. Examples of metabases and data classification operations are provided in U.S. Pat. Nos. 7,734,669 and 7,747,579.
Management and Reporting Operations
Certain embodiments leverage the integrated ubiquitous nature of system 100 to provide useful system-wide management and reporting. Operations management can generally include monitoring and managing the health and performance of system 100 by, without limitation, performing error tracking, generating granular storage/performance metrics (e.g., job success/failure information, deduplication efficiency, etc.), generating storage modeling and costing information, and the like. As an example, storage manager 140 or another component in system 100 may analyze traffic patterns and suggest and/or automatically route data to minimize congestion. In some embodiments, the system can generate predictions relating to storage operations or storage operation information. Such predictions, which may be based on a trending analysis, may predict various network operations or resource usage, such as network traffic levels, storage media use, use of bandwidth of communication links, use of media agent components, etc. Further examples of traffic analysis, trend analysis, prediction generation, and the like are described in U.S. Pat. No. 7,343,453.
In some configurations having a hierarchy of storage operation cells, a master storage manager 140 may track the status of subordinate cells, such as the status of jobs, system components, system resources, and other items, by communicating with storage managers 140 (or other components) in the respective storage operation cells. Moreover, the master storage manager 140 may also track status by receiving periodic status updates from the storage managers 140 (or other components) in the respective cells regarding jobs, system components, system resources, and other items. In some embodiments, a master storage manager 140 may store status information and other information regarding its associated storage operation cells and other system information in its management database 146 and/or index 150 (or in another location). The master storage manager 140 or other component may also determine whether certain storage-related or other criteria are satisfied, and may perform an action or trigger event (e.g., data migration) in response to the criteria being satisfied, such as where a storage threshold is met for a particular volume, or where inadequate protection exists for certain data. For instance, data from one or more storage operation cells is used to mitigate recognized risks dynamically and automatically, and/or to advise users of risks or suggest actions to mitigate these risks. For example, an information management policy may specify certain requirements (e.g., that a storage device should maintain a certain amount of free space, that secondary copies should occur at a particular interval, that data should be aged and migrated to other storage after a particular period, that data on a secondary volume should always have a certain level of availability and be restorable within a given time period, that data on a secondary volume may be mirrored or otherwise migrated to a specified number of other volumes, etc.). If a risk condition or other criterion is triggered, the system may notify the user of these conditions and may suggest (or automatically implement) a mitigation action to address the risk. For example, the system may indicate that data from a primary copy 112 should be migrated to a secondary storage device 108 to free up space on primary storage device 104. Examples of the use of risk factors and other triggering criteria are described in U.S. Pat. No. 7,343,453.
In some embodiments, system 100 may also determine whether a metric or other indication satisfies particular storage criteria sufficient to perform an action. For example, a storage policy or other definition might indicate that a storage manager 140 should initiate a particular action if a storage metric or other indication drops below or otherwise fails to satisfy specified criteria such as a threshold of data protection. In some embodiments, risk factors may be quantified into certain measurable service or risk levels. For example, certain applications and associated data may be considered to be more important relative to other data and services. Financial compliance data, for example, may be of greater importance than marketing materials, etc. Network administrators may assign priority values or “weights” to certain data and/or applications corresponding to the relative importance. The level of compliance of secondary copy operations specified for these applications may also be assigned a certain value. Thus, the health, impact, and overall importance of a service may be determined, such as by measuring the compliance value and calculating the product of the priority value and the compliance value to determine the “service level” and comparing it to certain operational thresholds to determine whether it is acceptable. Further examples of the service level determination are provided in U.S. Pat. No. 7,343,453.
System 100 may additionally calculate data costing and data availability associated with information management operation cells. For instance, data received from a cell may be used in conjunction with hardware-related information and other information about system elements to determine the cost of storage and/or the availability of particular data. Example information generated could include how fast a particular department is using up available storage space, how long data would take to recover over a particular pathway from a particular secondary storage device, costs over time, etc. Moreover, in some embodiments, such information may be used to determine or predict the overall cost associated with the storage of certain information. The cost associated with hosting a certain application may be based, at least in part, on the type of media on which the data resides, for example. Storage devices may be assigned to a particular cost categories, for example. Further examples of costing techniques are described in U.S. Pat. No. 7,343,453.
Any of the above types of information (e.g., information related to trending, predictions, job, cell or component status, risk, service level, costing, etc.) can generally be provided to users via user interface 158 in a single integrated view or console (not shown). Report types may include: scheduling, event management, media management and data aging. Available reports may also include backup history, data aging history, auxiliary copy history, job history, library and drive, media in library, restore history, and storage policy, etc., without limitation. Such reports may be specified and created at a certain point in time as a system analysis, forecasting, or provisioning tool. Integrated reports may also be generated that illustrate storage and performance metrics, risks and storage costing information. Moreover, users may create their own reports based on specific needs. User interface 158 can include an option to graphically depict the various components in the system using appropriate icons. As one example, user interface 158 may provide a graphical depiction of primary storage devices 104, secondary storage devices 108, data agents 142 and/or media agents 144, and their relationship to one another in system 100.
In general, the operations management functionality of system 100 can facilitate planning and decision-making. For example, in some embodiments, a user may view the status of some or all jobs as well as the status of each component of information management system 100. Users may then plan and make decisions based on this data. For instance, a user may view high-level information regarding secondary copy operations for system 100, such as job status, component status, resource status (e.g., communication pathways, etc.), and other information. The user may also drill down or use other means to obtain more detailed information regarding a particular component, job, or the like. Further examples are provided in U.S. Pat. No. 7,343,453.
System 100 can also be configured to perform system-wide e-discovery operations in some embodiments. In general, e-discovery operations provide a unified collection and search capability for data in the system, such as data stored in secondary storage devices 108 (e.g., backups, archives, or other secondary copies 116). For example, system 100 may construct and maintain a virtual repository for data stored in system 100 that is integrated across source applications 110, different storage device types, etc. According to some embodiments, e-discovery utilizes other techniques described herein, such as data classification and/or content indexing.
Information Management Policies
An information management policy 148 can include a data structure or other information source that specifies a set of parameters (e.g., criteria and rules) associated with secondary copy and/or other information management operations.
One type of information management policy 148 is a “storage policy.” According to certain embodiments, a storage policy generally comprises a data structure or other information source that defines (or includes information sufficient to determine) a set of preferences or other criteria for performing information management operations. Storage policies can include one or more of the following: (1) what data will be associated with the storage policy, e.g., subclient; (2) a destination to which the data will be stored; (3) datapath information specifying how the data will be communicated to the destination; (4) the type of secondary copy operation to be performed; and (5) retention information specifying how long the data will be retained at the destination (see, e.g.,
A storage policy can define where data is stored by specifying a target or destination storage device (or group of storage devices). For instance, where the secondary storage device 108 includes a group of disk libraries, the storage policy may specify a particular disk library for storing the subclients associated with the policy. As another example, where the secondary storage devices 108 include one or more tape libraries, the storage policy may specify a particular tape library for storing the subclients associated with the storage policy, and may also specify a drive pool and a tape pool defining a group of tape drives and a group of tapes, respectively, for use in storing the subclient data. While information in the storage policy can be statically assigned in some cases, some or all of the information in the storage policy can also be dynamically determined based on criteria set forth in the storage policy. For instance, based on such criteria, a particular destination storage device(s) or other parameter of the storage policy may be determined based on characteristics associated with the data involved in a particular secondary copy operation, device availability (e.g., availability of a secondary storage device 108 or a media agent 144), network status and conditions (e.g., identified bottlenecks), user credentials, and the like.
Datapath information can also be included in the storage policy. For instance, the storage policy may specify network pathways and components to utilize when moving the data to the destination storage device(s). In some embodiments, the storage policy specifies one or more media agents 144 for conveying data associated with the storage policy between the source and destination. A storage policy can also specify the type(s) of associated operations, such as backup, archive, snapshot, auxiliary copy, or the like. Furthermore, retention parameters can specify how long the resulting secondary copies 116 will be kept (e.g., a number of days, months, years, etc.), perhaps depending on organizational needs and/or compliance criteria.
When adding a new client computing device 102, administrators can manually configure information management policies 148 and/or other settings, e.g., via user interface 158. However, this can be an involved process resulting in delays, and it may be desirable to begin data protection operations quickly, without awaiting human intervention. Thus, in some embodiments, system 100 automatically applies a default configuration to client computing device 102. As one example, when one or more data agent(s) 142 are installed on a client computing device 102, the installation script may register the client computing device 102 with storage manager 140, which in turn applies the default configuration to the new client computing device 102. In this manner, data protection operations can begin substantially immediately. The default configuration can include a default storage policy, for example, and can specify any appropriate information sufficient to begin data protection operations. This can include a type of data protection operation, scheduling information, a target secondary storage device 108, data path information (e.g., a particular media agent 144), and the like.
Another type of information management policy 148 is a “scheduling policy,” which specifies when and how often to perform operations. Scheduling parameters may specify with what frequency (e.g., hourly, weekly, daily, event-based, etc.) or under what triggering conditions secondary copy or other information management operations are to take place. Scheduling policies in some cases are associated with particular components, such as a subclient, client computing device 102, and the like.
Another type of information management policy 148 is an “audit policy” (or “security policy”), which comprises preferences, rules and/or criteria that protect sensitive data in system 100. For example, an audit policy may define “sensitive objects” which are files or data objects that contain particular keywords (e.g., “confidential,” or “privileged”) and/or are associated with particular keywords (e.g., in metadata) or particular flags (e.g., in metadata identifying a document or email as personal, confidential, etc.). An audit policy may further specify rules for handling sensitive objects. As an example, an audit policy may require that a reviewer approve the transfer of any sensitive objects to a cloud storage site, and that if approval is denied for a particular sensitive object, the sensitive object should be transferred to a local primary storage device 104 instead. To facilitate this approval, the audit policy may further specify how a secondary storage computing device 106 or other system component should notify a reviewer that a sensitive object is slated for transfer.
Another type of information management policy 148 is a “provisioning policy,” which can include preferences, priorities, rules, and/or criteria that specify how client computing devices 102 (or groups thereof) may utilize system resources, such as available storage on cloud storage and/or network bandwidth. A provisioning policy specifies, for example, data quotas for particular client computing devices 102 (e.g., a number of gigabytes that can be stored monthly, quarterly or annually). Storage manager 140 or other components may enforce the provisioning policy. For instance, media agents 144 may enforce the policy when transferring data to secondary storage devices 108. If a client computing device 102 exceeds a quota, a budget for the client computing device 102 (or associated department) may be adjusted accordingly or an alert may trigger.
While the above types of information management policies 148 are described as separate policies, one or more of these can be generally combined into a single information management policy 148. For instance, a storage policy may also include or otherwise be associated with one or more scheduling, audit, or provisioning policies or operational parameters thereof. Moreover, while storage policies are typically associated with moving and storing data, other policies may be associated with other types of information management operations. The following is a non-exhaustive list of items that information management policies 148 may specify:
Information management policies 148 can additionally specify or depend on historical or current criteria that may be used to determine which rules to apply to a particular data object, system component, or information management operation, such as:
Example Storage Policy and Secondary Copy Operations
As indicated by the dashed box, the second media agent 144B and tape library 108B are “off-site,” and may be remotely located from the other components in system 100 (e.g., in a different city, office building, etc.). Indeed, “off-site” may refer to a magnetic tape located in remote storage, which must be manually retrieved and loaded into a tape drive to be read. In this manner, information stored on the tape library 108B may provide protection in the event of a disaster or other failure at the main site(s) where data is stored.
The file system subclient 112A in certain embodiments generally comprises information generated by the file system and/or operating system of client computing device 102, and can include, for example, file system data (e.g., regular files, file tables, mount points, etc.), operating system data (e.g., registries, event logs, etc.), and the like. The e-mail subclient 112B can include data generated by an e-mail application operating on client computing device 102, e.g., mailbox information, folder information, emails, attachments, associated database information, and the like. As described above, the subclients can be logical containers, and the data included in the corresponding primary data 112A and 112B may or may not be stored contiguously.
The example storage policy 148A includes backup copy preferences or rule set 160, disaster recovery copy preferences or rule set 162, and compliance copy preferences or rule set 164. Backup copy rule set 160 specifies that it is associated with file system subclient 166 and email subclient 168. Each of subclients 166 and 168 are associated with the particular client computing device 102. Backup copy rule set 160 further specifies that the backup operation will be written to disk library 108A and designates a particular media agent 144A to convey the data to disk library 108A. Finally, backup copy rule set 160 specifies that backup copies created according to rule set 160 are scheduled to be generated hourly and are to be retained for 30 days. In some other embodiments, scheduling information is not included in storage policy 148A and is instead specified by a separate scheduling policy.
Disaster recovery copy rule set 162 is associated with the same two subclients 166 and 168. However, disaster recovery copy rule set 162 is associated with tape library 108B, unlike backup copy rule set 160. Moreover, disaster recovery copy rule set 162 specifies that a different media agent, namely 144B, will convey data to tape library 108B. Disaster recovery copies created according to rule set 162 will be retained for 60 days and will be generated daily. Disaster recovery copies generated according to disaster recovery copy rule set 162 can provide protection in the event of a disaster or other catastrophic data loss that would affect the backup copy 116A maintained on disk library 108A.
Compliance copy rule set 164 is only associated with the email subclient 168, and not the file system subclient 166. Compliance copies generated according to compliance copy rule set 164 will therefore not include primary data 112A from the file system subclient 166. For instance, the organization may be under an obligation to store and maintain copies of email data for a particular period of time (e.g., 10 years) to comply with state or federal regulations, while similar regulations do not apply to file system data. Compliance copy rule set 164 is associated with the same tape library 108B and media agent 144B as disaster recovery copy rule set 162, although a different storage device or media agent could be used in other embodiments. Finally, compliance copy rule set 164 specifies that the copies it governs will be generated quarterly and retained for 10 years.
Secondary Copy Jobs
A logical grouping of secondary copy operations governed by a rule set and being initiated at a point in time may be referred to as a “secondary copy job” (and sometimes may be called a “backup job,” even though it is not necessarily limited to creating only backup copies). Secondary copy jobs may be initiated on demand as well. Steps 1-9 below illustrate three secondary copy jobs based on storage policy 148A.
Referring to
At step 2, file system data agent 142A and email data agent 142B on client computing device 102 respond to instructions from storage manager 140 by accessing and processing the respective subclient primary data 112A and 112B involved in the backup copy operation, which can be found in primary storage device 104. Because the secondary copy operation is a backup copy operation, the data agent(s) 142A, 142B may format the data into a backup format or otherwise process the data suitable for a backup copy.
At step 3, client computing device 102 communicates the processed file system data (e.g., using file system data agent 142A) and the processed email data (e.g., using email data agent 142B) to the first media agent 144A according to backup copy rule set 160, as directed by storage manager 140. Storage manager 140 may further keep a record in management database 146 of the association between media agent 144A and one or more of: client computing device 102, file system subclient 112A, file system data agent 142A, email subclient 112B, email data agent 142B, and/or backup copy 116A.
The target media agent 144A receives the data-agent-processed data from client computing device 102, and at step 4 generates and conveys backup copy 116A to disk library 108A to be stored as backup copy 116A, again at the direction of storage manager 140 and according to backup copy rule set 160. Media agent 144A can also update its index 153 to include data and/or metadata related to backup copy 116A, such as information indicating where the backup copy 116A resides on disk library 108A, where the email copy resides, where the file system copy resides, data and metadata for cache retrieval, etc. Storage manager 140 may similarly update its index 150 to include information relating to the secondary copy operation, such as information relating to the type of operation, a physical location associated with one or more copies created by the operation, the time the operation was performed, status information relating to the operation, the components involved in the operation, and the like. In some cases, storage manager 140 may update its index 150 to include some or all of the information stored in index 153 of media agent 144A. At this point, the backup job may be considered complete. After the 30-day retention period expires, storage manager 140 instructs media agent 144A to delete backup copy 116A from disk library 108A and indexes 150 and/or 153 are updated accordingly.
At step 5, storage manager 140 initiates another backup job for a disaster recovery copy according to the disaster recovery rule set 162. Illustratively this includes steps 5-7 occurring daily for creating disaster recovery copy 116B. Illustratively, and by way of illustrating the scalable aspects and off-loading principles embedded in system 100, disaster recovery copy 116B is based on backup copy 116A and not on primary data 112A and 112B.
At step 6, illustratively based on instructions received from storage manager 140 at step 5, the specified media agent 1446 retrieves the most recent backup copy 116A from disk library 108A.
At step 7, again at the direction of storage manager 140 and as specified in disaster recovery copy rule set 162, media agent 144B uses the retrieved data to create a disaster recovery copy 116B and store it to tape library 1086. In some cases, disaster recovery copy 116B is a direct, mirror copy of backup copy 116A, and remains in the backup format. In other embodiments, disaster recovery copy 116B may be further compressed or encrypted, or may be generated in some other manner, such as by using primary data 112A and 1126 from primary storage device 104 as sources. The disaster recovery copy operation is initiated once a day and disaster recovery copies 1166 are deleted after 60 days; indexes 153 and/or 150 are updated accordingly when/after each information management operation is executed and/or completed. The present backup job may be considered completed.
At step 8, storage manager 140 initiates another backup job according to compliance rule set 164, which performs steps 8-9 quarterly to create compliance copy 116C. For instance, storage manager 140 instructs media agent 144B to create compliance copy 116C on tape library 1086, as specified in the compliance copy rule set 164.
At step 9 in the example, compliance copy 116C is generated using disaster recovery copy 1166 as the source. This is efficient, because disaster recovery copy resides on the same secondary storage device and thus no network resources are required to move the data. In other embodiments, compliance copy 116C is instead generated using primary data 112B corresponding to the email subclient or using backup copy 116A from disk library 108A as source data. As specified in the illustrated example, compliance copies 116C are created quarterly, and are deleted after ten years, and indexes 153 and/or 150 are kept up-to-date accordingly.
Again referring to
Information governance policies allow administrators to obtain different perspectives on an organization's online and offline data, without the need for a dedicated data silo created solely for each different viewpoint. As described previously, the data storage systems herein build an index that reflects the contents of a distributed data set that spans numerous clients and storage devices, including both primary data and secondary copies, and online and offline copies. An organization may apply multiple information governance policies in a top-down manner over that unified data set and indexing schema in order to view and manipulate the data set through different lenses, each of which is adapted to a particular compliance or business goal. Thus, for example, by applying an e-discovery policy and a Sarbanes-Oxley policy, two different groups of users in an organization can conduct two very different analyses of the same underlying physical set of data/copies, which may be distributed throughout the information management system.
An information governance policy may comprise a classification policy, which defines a taxonomy of classification terms or tags relevant to a compliance task and/or business objective. A classification policy may also associate a defined tag with a classification rule. A classification rule defines a particular combination of criteria, such as users who have created, accessed or modified a document or data object; file or application types; content or metadata keywords; clients or storage locations; dates of data creation and/or access; review status or other status within a workflow (e.g., reviewed or un-reviewed); modification times or types of modifications; and/or any other data attributes in any combination, without limitation. A classification rule may also be defined using other classification tags in the taxonomy. The various criteria used to define a classification rule may be combined in any suitable fashion, for example, via Boolean operators, to define a complex classification rule. As an example, an e-discovery classification policy might define a classification tag “privileged” that is associated with documents or data objects that (1) were created or modified by legal department staff, or (2) were sent to or received from outside counsel via email, or (3) contain one of the following keywords: “privileged” or “attorney” or “counsel,” or other like terms. Accordingly, all these documents or data objects will be classified as “privileged.”
One specific type of classification tag, which may be added to an index at the time of indexing, is an “entity tag.” An entity tag may be, for example, any content that matches a defined data mask format. Examples of entity tags might include, e.g., social security numbers (e.g., any numerical content matching the formatting mask XXX-XX-XXXX), credit card numbers (e.g., content having a 13-16 digit string of numbers), SKU numbers, product numbers, etc. A user may define a classification policy by indicating criteria, parameters or descriptors of the policy via a graphical user interface, such as a form or page with fields to be filled in, pull-down menus or entries allowing one or more of several options to be selected, buttons, sliders, hypertext links or other known user interface tools for receiving user input, etc. For example, a user may define certain entity tags, such as a particular product number or project ID. In some implementations, the classification policy can be implemented using cloud-based techniques. For example, the storage devices may be cloud storage devices, and the storage manager 140 may execute cloud service provider API over a network to classify data stored on cloud storage devices.
Restore Operations from Secondary Copies
While not shown in
As one example, a user may manually initiate a restore of backup copy 116A, e.g., by interacting with user interface 158 of storage manager 140 or with a web-based console with access to system 100. Storage manager 140 may accesses data in its index 150 and/or management database 146 (and/or the respective storage policy 148A) associated with the selected backup copy 116A to identify the appropriate media agent 144A and/or secondary storage device 108A where the secondary copy resides. The user may be presented with a representation (e.g., stub, thumbnail, listing, etc.) and metadata about the selected secondary copy, in order to determine whether this is the appropriate copy to be restored, e.g., date that the original primary data was created. Storage manager 140 will then instruct media agent 144A and an appropriate data agent 142 on the target client computing device 102 to restore secondary copy 116A to primary storage device 104. A media agent may be selected for use in the restore operation based on a load balancing algorithm, an availability based algorithm, or other criteria. The selected media agent, e.g., 144A, retrieves secondary copy 116A from disk library 108A. For instance, media agent 144A may access its index 153 to identify a location of backup copy 116A on disk library 108A, or may access location information residing on disk library 108A itself.
In some cases a backup copy 116A that was recently created or accessed, may be cached to speed up the restore operation. In such a case, media agent 144A accesses a cached version of backup copy 116A residing in index 153, without having to access disk library 108A for some or all of the data. Once it has retrieved backup copy 116A, the media agent 144A communicates the data to the requesting client computing device 102. Upon receipt, file system data agent 142A and email data agent 142B may unpack (e.g., restore from a backup format to the native application format) the data in backup copy 116A and restore the unpackaged data to primary storage device 104. In general, secondary copies 116 may be restored to the same volume or folder in primary storage device 104 from which the secondary copy was derived; to another storage location or client computing device 102; to shared storage, etc. In some cases, the data may be restored so that it may be used by an application 110 of a different version/vintage from the application that created the original primary data 112.
Example Secondary Copy Formatting
The formatting and structure of secondary copies 116 can vary depending on the embodiment. In some cases, secondary copies 116 are formatted as a series of logical data units or “chunks” (e.g., 512 MB, 1 GB, 2 GB, 4 GB, or 8 GB chunks). This can facilitate efficient communication and writing to secondary storage devices 108, e.g., according to resource availability. For example, a single secondary copy 116 may be written on a chunk-by-chunk basis to one or more secondary storage devices 108. In some cases, users can select different chunk sizes, e.g., to improve throughput to tape storage devices. Generally, each chunk can include a header and a payload. The payload can include files (or other data units) or subsets thereof included in the chunk, whereas the chunk header generally includes metadata relating to the chunk, some or all of which may be derived from the payload. For example, during a secondary copy operation, media agent 144, storage manager 140, or other component may divide files into chunks and generate headers for each chunk by processing the files. Headers can include a variety of information such as file and/or volume identifier(s), offset(s), and/or other information associated with the payload data items, a chunk sequence number, etc. Importantly, in addition to being stored with secondary copy 116 on secondary storage device 108, chunk headers can also be stored to index 153 of the associated media agent(s) 144 and/or to index 150 associated with storage manager 140. This can be useful for providing faster processing of secondary copies 116 during browsing, restores, or other operations. In some cases, once a chunk is successfully transferred to a secondary storage device 108, the secondary storage device 108 returns an indication of receipt, e.g., to media agent 144 and/or storage manager 140, which may update their respective indexes 153, 150 accordingly. During restore, chunks may be processed (e.g., by media agent 144) according to the information in the chunk header to reassemble the files.
Data can also be communicated within system 100 in data channels that connect client computing devices 102 to secondary storage devices 108. These data channels can be referred to as “data streams,” and multiple data streams can be employed to parallelize an information management operation, improving data transfer rate, among other advantages. Example data formatting techniques including techniques involving data streaming, chunking, and the use of other data structures in creating secondary copies are described in U.S. Pat. Nos. 7,315,923, 8,156,086, and 8,578,120.
Referring to
As an example, data structures 180 illustrated in
If the operating system of the secondary storage computing device 106 on which media agent 144 operates supports sparse files, then when media agent 144 creates container files 190/191/193, it can create them as sparse files. A sparse file is a type of file that may include empty space (e.g., a sparse file may have real data within it, such as at the beginning of the file and/or at the end of the file, but may also have empty space in it that is not storing actual data, such as a contiguous range of bytes all having a value of zero). Having container files 190/191/193 be sparse files allows media agent 144 to free up space in container files 190/191/193 when blocks of data in container files 190/191/193 no longer need to be stored on the storage devices. In some examples, media agent 144 creates a new container file 190/191/193 when a container file 190/191/193 either includes 100 blocks of data or when the size of the container file 190 exceeds 50 MB. In other examples, media agent 144 creates a new container file 190/191/193 when a container file 190/191/193 satisfies other criteria (e.g., it contains from approx. 100 to approx. 1000 blocks or when its size exceeds approximately 50 MB to 1 GB). In some cases, a file on which a secondary copy operation is performed may comprise a large number of data blocks. For example, a 100 MB file may comprise 400 data blocks of size 256 KB. If such a file is to be stored, its data blocks may span more than one container file, or even more than one chunk folder. As another example, a database file of 20 GB may comprise over 40,000 data blocks of size 512 KB. If such a database file is to be stored, its data blocks will likely span multiple container files, multiple chunk folders, and potentially multiple volume folders. Restoring such files may require accessing multiple container files, chunk folders, and/or volume folders to obtain the requisite data blocks.
Using Backup Data for Replication and Disaster Recovery (“Live Synchronization”)
There is an increased demand to off-load resource intensive information management tasks (e.g., data replication tasks) away from production devices (e.g., physical or virtual client computing devices) in order to maximize production efficiency. At the same time, enterprises expect access to readily-available up-to-date recovery copies in the event of failure, with little or no production downtime.
The synchronization can be achieved by generally applying an ongoing stream of incremental backups from the source subsystem 201 to the destination subsystem 203, such as according to what can be referred to as an “incremental forever” approach.
As shown, the data can be copied from source to destination in an incremental fashion, such that only changed blocks are transmitted, and in some cases multiple incremental backups are consolidated at the source so that only the most current changed blocks are transmitted to and applied at the destination. An example of live synchronization of virtual machines using the “incremental forever” approach is found in U.S. Pat. No. 10,228,962 entitled “Live Synchronization and Management of Virtual Machines across Computing and Virtualization Platforms and Using Live Synchronization to Support Disaster Recovery.” Moreover, a deduplicated copy can be employed to further reduce network traffic from source to destination. For instance, the system can utilize the deduplicated copy techniques described in U.S. Pat. No. 9,239,687, entitled “Systems and Methods for Retaining and Using Data Block Signatures in Data Protection Operations.”
At step 4, destination media agent(s) 244b write the received backup/secondary copy data to the destination secondary storage device(s) 208b. At step 5, the synchronization is completed when the destination media agent(s) and destination data agent(s) 242b restore the backup/secondary copy data to the destination client computing device(s) 202b. The destination client computing device(s) 202b may be kept “warm” awaiting activation in case failure is detected at the source. This synchronization/replication process can incorporate the techniques described in U.S. Patent Pub. No. 2016/0350391 entitled “Replication Using Deduplicated Secondary Copy Data.”
Where the incremental backups are applied on a frequent, on-going basis, the synchronized copies can be viewed as mirror or replication copies. Moreover, by applying the incremental backups to the destination site 203 using backup or other secondary copy data, the production site 201 is not burdened with the synchronization operations. Because the destination site 203 can be maintained in a synchronized “warm” state, the downtime for switching over from the production site 201 to the destination site 203 is substantially less than with a typical restore from secondary storage. Thus, the production site 201 may flexibly and efficiently fail over, with minimal downtime and with relatively up-to-date data, to a destination site 203, such as a cloud-based failover site. The destination site 203 can later be reverse synchronized back to the production site 201, such as after repairs have been implemented or after the failure has passed.
Integrating with the Cloud Using File System Protocols
Given the ubiquity of cloud computing, it can be increasingly useful to provide data protection and other information management services in a scalable, transparent, and highly plug-able fashion.
Where NFS is used, for example, secondary storage subsystem 218 allocates an NFS network path to the client computing device 202 or to one or more target applications 210 running on client computing device 202. During a backup or other secondary copy operation, the client computing device 202 mounts the designated NFS path and writes data to that NFS path. The NFS path may be obtained from NFS path data 215 stored locally at the client computing device 202, and which may be a copy of or otherwise derived from NFS path data 219 stored in the secondary storage subsystem 218.
Write requests issued by client computing device(s) 202 are received by data agent 242 in secondary storage subsystem 218, which translates the requests and works in conjunction with media agent 244 to process and write data to a secondary storage device(s) 208, thereby creating a backup or other secondary copy. Storage manager 240 can include a pseudo-client manager 217, which coordinates the process by, among other things, communicating information relating to client computing device 202 and application 210 (e.g., application type, client computing device identifier, etc.) to data agent 242, obtaining appropriate NFS path data from the data agent 242 (e.g., NFS path information), and delivering such data to client computing device 202.
Conversely, during a restore or recovery operation client computing device 202 reads from the designated NFS network path, and the read request is translated by data agent 242. The data agent 242 then works with media agent 244 to retrieve, re-process (e.g., re-hydrate, decompress, decrypt), and forward the requested data to client computing device 202 using NFS.
By moving specialized software associated with system 200 such as data agent 242 off the client computing devices 202, the illustrative architecture effectively decouples the client computing devices 202 from the installed components of system 200, improving both scalability and plug-ability of system 200. Indeed, the secondary storage subsystem 218 in such environments can be treated simply as a read/write NFS target for primary storage subsystem 217, without the need for information management software to be installed on client computing devices 202. As one example, an enterprise implementing a cloud production computing environment can add VM client computing devices 202 without installing and configuring specialized information management software on these VMs. Rather, backups and restores are achieved transparently, where the new VMs simply write to and read from the designated NFS path. An example of integrating with the cloud using file system protocols or so-called “infinite backup” using NFS share is found in U.S. Patent Pub. No. 2017/0235647 entitled “Data Protection Operations Based on Network Path Information.” Examples of improved data restoration scenarios based on network-path information, including using stored backups effectively as primary data sources, may be found in U.S. Pat. No. 10,684,924 entitled “Data Restoration Operations Based on Network Path Information.”
Highly Scalable Managed Data Pool Architecture
Enterprises are seeing explosive data growth in recent years, often from various applications running in geographically distributed locations.
The illustrated system 200 includes a grid 245 of media agents 244 logically organized into a control tier 231 and a secondary or storage tier 233. Media agents assigned to the storage tier 233 can be configured to manage a secondary storage pool 208 as a deduplication store, and be configured to receive client write and read requests from the primary storage subsystem 217, and direct those requests to the secondary tier 233 for servicing. For instance, media agents CMA1-CMA3 in the control tier 231 maintain and consult one or more deduplication databases 247, which can include deduplication information (e.g., data block hashes, data block links, file containers for deduplicated files, etc.) sufficient to read deduplicated files from secondary storage pool 208 and write deduplicated files to secondary storage pool 208. For instance, system 200 can incorporate any of the deduplication systems and methods shown and described in U.S. Pat. No. 9,020,900, entitled “Distributed Deduplicated Storage System,” and U.S. Pat. No. 9,633,033 entitled “High Availability Distributed Deduplicated Storage System.”
Media agents SMA1-SMA6 assigned to the secondary tier 233 receive write and read requests from media agents CMA1-CMA3 in control tier 231, and access secondary storage pool 208 to service those requests. Media agents CMA1-CMA3 in control tier 231 can also communicate with secondary storage pool 208, and may execute read and write requests themselves (e.g., in response to requests from other control media agents CMA1-CMA3) in addition to issuing requests to media agents in secondary tier 233. Moreover, while shown as separate from the secondary storage pool 208, deduplication database(s) 247 can in some cases reside in storage devices in secondary storage pool 208.
As shown, each of the media agents 244 (e.g., CMA1-CMA3, SMA1-SMA6, etc.) in grid 245 can be allocated a corresponding dedicated partition 251A-2511, respectively, in secondary storage pool 208. Each partition 251 can include a first portion 253 containing data associated with (e.g., stored by) media agent 244 corresponding to the respective partition 251. System 200 can also implement a desired level of replication, thereby providing redundancy in the event of a failure of a media agent 244 in grid 245. Along these lines, each partition 251 can further include a second portion 255 storing one or more replication copies of the data associated with one or more other media agents 244 in the grid.
System 200 can also be configured to allow for seamless addition of media agents 244 to grid 245 via automatic configuration. As one illustrative example, a storage manager (not shown) or other appropriate component may determine that it is appropriate to add an additional node to control tier 231, and perform some or all of the following: (i) assess the capabilities of a newly added or otherwise available computing device as satisfying a minimum criteria to be configured as or hosting a media agent in control tier 231; (ii) confirm that a sufficient amount of the appropriate type of storage exists to support an additional node in control tier 231 (e.g., enough disk drive capacity exists in storage pool 208 to support an additional deduplication database 247); (iii) install appropriate media agent software on the computing device and configure the computing device according to a pre-determined template; (iv) establish a partition 251 in the storage pool 208 dedicated to the newly established media agent 244; and (v) build any appropriate data structures (e.g., an instance of deduplication database 247). An example of highly scalable managed data pool architecture or so-called web-scale architecture for storage and data management is found in U.S. Pat. No. 10,255,143 entitled “Deduplication Replication In A Distributed Deduplication Data Storage System.”
The embodiments and components thereof disclosed in
Cloud Computing.
The National Institute of Standards and Technology (NIST) provides the following definition of Cloud Computing characteristics, service models, and deployment models:
Cloud Computing
Essential Characteristics:
Service Models:
Deployment Models:
Source:
Peter Mell, Timothy Grance (September 2011). The NIST Definition of Cloud Computing, National Institute of Standards and Technology: U.S. Department of Commerce. Special publication 800-145. nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf (accessed 26 Apr. 2019). Cloud computing aims to allow those who consume the services (whether individuals or organizations) to benefit from the available technologies without the need for deep knowledge about or expertise with each of them. Wikipedia, Cloud Computing, en.wikipedia.org/wiki/Cloud_computing (accessed 26 Apr. 2019). “Cloud computing metaphor: the group of networked elements providing services need not be individually addressed or managed by users; instead, the entire provider-managed suite of hardware and software can be thought of as an amorphous cloud.” Id.
Cloud Service Accounts and Variability in Cloud Services.
Cloud service providers such as Amazon, Microsoft, Alibaba, Google, Salesforce, Cisco, etc. provide access to their particular cloud services via cloud service accounts, such as corporate accounts, departmental accounts, individual user accounts, etc. Each cloud service account typically has authentication features, e.g., passwords, certificates, etc., to restrict and control access to the cloud service. Each account also might have service level guarantees and/or other terms and conditions between the cloud service provider and the service subscriber, e.g., a company, a government agency, an individual user. A subscribing entity might have multiple accounts with a cloud service provider, such as an account for the Engineering department, an account for the Finance department, an account for the Human Resources department, other accounts for individual company users, etc., without limitation. Each cloud service account carries different authentication, even though the services subscriber is the same entity.
Different cloud service accounts might differ not just in service level guarantees, but might include different services. For example, one account might include long-term storage resources, whereas another account might be limited to ordinary data storage. For example, some accounts might have access to data processing functions supplied by the cloud service provider, such as machine learning algorithms, statistical analysis packages, etc., whereas other accounts might lack such features. Accordingly, the resources available to the user(s) of cloud service accounts can vary as between accounts, even if the accounts have the same subscriber and the same cloud service provider. Thus, the user experience and the technologies available as between cloud service accounts can vary significantly. Thus, when considering cloud computing, the specifics of cloud service accounts can play a role in the availability and/or portability of resources. Crossing account boundaries can pose technological barriers when considering migration of applications and their cloud services assets.
Cloud Availability Zones.
“Availability zones (AZs) are isolated locations within . . . regions from which public cloud services originate and operate. Regions are geographic locations in which public cloud service providers' data centers reside. Businesses choose one or multiple worldwide availability zones for their services depending on business needs. Businesses select availability zones for a variety of reasons, including compliance and proximity to end customers. Cloud administrators can also choose to replicate services across multiple availability zones to decrease latency or protect resources. Admins can move resources to another availability zone in the event of an outage. Certain cloud services may also be limited to particular regions or AZs.” Source: Margaret Rouse, Definition of Availability Zones, TechTarget, searchaws.techtarget.com/definition/availability-zones (accessed 26 Apr. 2019).
Here is a vendor-specific example of how cloud service availability zones are organized in the Google Cloud: “Certain [Google] Compute Engine resources live in regions or zones. A region is a specific geographical location where you can run your resources. Each region has one or more zones; most regions have three or more zones. For example, the us-central1 region denotes a region in the Central United States that has zones us-central1-a, us-central1-b, us-central1-c, and us-central1-f. Resources that live in a zone, such as instances or persistent disks, are referred to as zonal resources. Other resources, like static external IP addresses, are regional. Regional resources can be used by any resources in that region, regardless of zone, while zonal resources can only be used by other resources in the same zone. For example, disks and instances are both zonal resources. To attach a disk to an instance, both resources must be in the same zone. Similarly, if you want to assign a static IP address to an instance, the instance must be in the same region as the static IP. Only certain resources are region- or zone-specific. Other resources, such as images, are global resources that can be used by any other resources across any location. For information on global, regional, and zonal Compute Engine resources, see Global, Regional, and Zonal Resources.” Source: Google Cloud Regions and Zones, cloud.google.com/compute/docs/regions-zones/ (accessed 26 Apr. 2019) (emphasis added).
Accordingly, when considering cloud computing, availability zones can play a role in the availability and/or portability of resources. Crossing zone boundaries can pose technological barriers when considering migration of applications and their cloud service assets, even when the different availability zones are supplied by the same cloud service provider.
Traditional Non-Cloud (“On-Premises”) Data Centers are Distinguishable from Cloud Computing.
Traditional data centers generally do not have cloud computing characteristics. For example, the user experience is generally different, for example in regard to the name space(s) used for the storage, computing, and network resources. Moreover, substantial increases in resources needed by a user are not provisioned on demand. A traditional data center is physically located within the enterprise/organization that owns it. A traditional non-cloud data center might comprise computing resources such as servers, mainframes, virtual servers/clusters, etc.; and/or data storage resources, such as network-attached storage, storage area networks, tape libraries, etc. The owner of the traditional data center procures hardware, software, and network infrastructure (including making the associated capital investments); and manages going-forward planning for the data center. A traditional data center is staffed by professional Information Technology (IT) personnel, who are responsible for the data center's configuration, operation, upgrades, and maintenance. Thus, a traditional non-cloud data center can be thought of as self-managed by its owner/operator for the benefit of in-house users, as compared to cloud computing, which is managed by the cloud service provider and supplied as a service to outside subscribers. Clearly, a cloud computing service also has hardware, software, and networking infrastructure and professionals staffing it, as well as having an owner responsible for housing and paying for the infrastructure. However, the cloud computing service is consumed differently, served differently, and deployed differently compared to non-cloud data centers. Traditional non-cloud data centers are sometimes referred to as “on-premises” data centers, because their facilities are literally within the bounds of the organization that owns the data center. Cloud service providers' data centers generally are not within the bounds of the subscriber organization and are consumed “at a distance” “in the cloud.”
Accordingly, when considering cloud computing versus non-cloud data center deployment, the choice can play a role in the availability and/or portability of resources. Crossing boundaries between non-cloud data centers and cloud computing can pose technological barriers. For example, storing a database at a non-cloud data center might require different resources and/or access features/controls than storing the database at a cloud computing service. Thus, moving the database from the non-cloud data center to a cloud service account may require data conversion, re-configuration, and/or adaptation that go above and beyond merely copying the database. Likewise for virtual machines (VMs). Conversely, moving data, applications, VMs, and/or web services from cloud computing to a non-cloud data center also can involve data conversion, re-configuration, and/or adaptation to ensure success.
Service Models.
Differences in service models, comparing non-cloud “on-premises” data centers versus IaaS versus PaaS versus SaaS, can yield different performance and cost profiles. Different service models can affect resource availability and/or portability of distributed/serverless applications, at least because the management of different resources rests with different providers and governed by different terms and conditions. See, e.g., Stephen Watts, SaaS vs PaaS vs IaaS: What's The Difference and How To Choose, BMC Blogs, BMC Software, Inc., www.bmc.com/blogs/saas-vs-paas-vs-iaas-whats-the-difference-and-how-to-choose/ (accessed 26 Apr. 2019).
In regard to the figures described herein, other embodiments are possible within the scope of the present invention, such that the above-recited components, steps, blocks, operations, messages, requests, queries, and/or instructions are differently arranged, sequenced, sub-divided, organized, and/or combined. In some embodiments, a different component may initiate or execute a given operation.
Live Mount of Virtual Machines in a Public Cloud Computing Environment
Live Mount is illustratively invoked by a storage manager that is generally responsible for managing data storage, storage operations, and information management in the illustrative data storage management system. Providing live mount capabilities in public cloud computing environments such as Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, etc. is not possible in the prior art, because of very restrictive boot environments that do not allow Preboot eXecution Environment (e.g., PXE, iPXE) technologies for booting of non-native VM disks from external sources such as 3DFS1 from Commvault Systems, Inc. Thus, even though a data agent (e.g., the so-called Virtual Server Agent (VSA) from Commvault Systems, Inc.) can back up virtual machines (VMs), including cloud-based VMs, the prior art cannot provide the ability to live mount these non-cloud-native backup copies in a public cloud computing environment.
To overcome this significant deficiency of the prior art, the present inventors devised an approach of gaining control over I/O to/from VM virtual disks, including the operating system (OS) disk and one or more data disks. Coupled with the novel components and capabilities of the illustrative data storage management system disclosed herein, this approach enables VMs to be live mounted in a public cloud computing environment using non-cloud-native backup copies.
In some embodiments, snapshots of the original VM may be available in the destination cloud computing environment and may be used as an alternative data source instead of the backup copies. In some examples, cloud-based VM snapshots will be called out separately, while in others it is to be understood that the illustrative data storage management system comprises features for using cloud-based VM snapshots as an
Data storage management system 300 is analogous to system 100 and is further enhanced with features and/or components for live mounting VMs in a public cloud computing environment as described herein. Illustratively, system 300 comprises all depicted components in the present figure except for public cloud computing environment 301, but the invention is not so limited.
VM backup copy 116 is a secondary copy of a VM, the backup copy created by system 300. Illustratively, media agent 344 generated VM backup copy 166 and stored it to secondary storage device (backup storage, backup media) 108. VM backup copy 116 is a data source for live mounted VM 398.
Public cloud computing environment 301 is a cloud computing environment supplied by a cloud service provider such as Amazon (e.g., Amazon Web Services), Microsoft (e.g., Azure Cloud), Google (e.g., Google Cloud Platform), etc. Public cloud computing environment 301 is well known in the art.
Cloud-based VM snapshot 316 is a secondary copy of a VM. Snapshot 316 is initiated by system 300 and is stored as a cloud-native snapshot within public cloud computing environment 301. In some embodiments, snapshot 316 is used as an alternative to VM backup copy 116 as a data source for live-mounted VM 398. The disclosed solutions use proprietary components of systems 300/400 to live-mount from the snapshot in a manner consistent with how the non-cloud-native backup copies are live-mounted. This approach enables a streamlined approach, across technologies, that allows system 300 to live mount any number of backup copies 116 and/or snapshots 316 consistently, providing customers with a unified service that is agnostic, from the user's perspective of whether the data source is a cloud-native snapshot 316 or a non-cloud-native backup copy 116. Likewise in regard to live recovery by system 400.
Storage manager 340 is analogous to storage manager 140 and additionally comprises features for operating in system 300 and/or system 400 (see
Media agent 344 is analogous to media agent 144 and additionally comprises features for operating in system 300 and/or system 400, such as communicating with recovery computing device 350/450. In some embodiments, the media agent executes on a distinct computing device separate from the recovery computing device 350/450, but the invention is not so limited, and in some embodiments these two components may be co-resident. In some embodiments, the media agent is topographically close to the backup media from a networking perspective, i.e., outside the VM host cloud 301.
Recovery computing device 350 is a computing device that comprises one or more hardware processors and computer memory for executing computer instructions. Recovery computing device is configured as a computing resource in cloud computing environment 301. Recovery computing device 350 executes specialized software for operating as a component of system 300. More details are given in
Machine image pool 360 is a pre-packaged (or pre-configured) pool of machine images or operating system images (e.g., Amazon machine image (AMI), etc.). This “machine image pool” is configured in the VM host cloud (e.g., 301) as a persistent resource for future live mount and live recovery operations. Each image comprises utilities for loading a root file system into a target VM such as the live-mounted VM, and further comprises key drivers for setting up the VM's virtual disk, such as an Network File System (NFS) driver and a network interface card (NIC) driver; the NIC driver is accompanied by a script for registering Internet Small Computer Systems Interface (iSCSI) disks. The pool comprises custom pre-created initramfs (or equivalent technologies) built with NFS, NIC drivers with a script to register iSCSI disks. The machine image pool is tapped when a VM is to be live-mounted or temp-mounted in the public cloud computing environment 301. Accordingly, a proper machine image is selected that is suitable to the configuration of the target VM, e.g., having a certain version of a certain operating system, etc. This pool is pre-created in the public cloud computing environment 301 before the live mount and/or live recovery operation is initiated.
Live-mounted VM 398 is a cloud-based virtual machine configured in public cloud computing environment 301. This VM is the target VM for live-mounting VM backup copy 116 or VM snapshot 316 as described herein.
Recovery process 352 is an illustrative process that executes on recovery computing device 350 and communicates with storage manager 340. Recovery process 352 illustratively comprises recall thread 354 and in some embodiments also comprises pseudo-disk driver 356, but the invention is not so limited. The recovery process coordinates some aspects of the live mount operation, such as invoking a pseudo-disk driver 356 corresponding to each VM virtual disk of the source VM, causing the pseudo-disk driver 356 to set up the corresponding pseudo-disk 358, selecting a suitable machine image 362 from the machine image pool 360, causing a VM 398/498 to be instantiated for the live-mount/temp-mount operation. In the live recovery operation (see
Recall thread 354 is an illustrative thread that runs within recovery process 352 and which received requests for extents from pseudo-disk driver 356, which intercepts read requests from VM 398. Recall thread 354 is responsible for obtaining extents from VM backup copy 116 and/or from cloud-based VM snapshot 316. To accomplish the former, recall thread 354 requests extents from media agent 344, which uses its backup index 153 to locate the requested extent on backup media 108, extract the extent from VM backup copy 116, process it if necessary (e.g., decrypt, decompress, etc.), and transmit the result to recall thread 354. To accomplish the latter, recall thread 354 is configured to use cloud-native application programming interface(s) (API) to extract extents from cloud-based VM snapshot 316. Recall thread 354 transmits the recalled (extracted, recovered, restored) extents to pseudo-disk driver 356.
Pseudo-disk driver 356 is a driver that executes on recovery computing device 350/450. The illustrative pseudo-disk driver 356 interoperates with the media agent 344 and with the live-mounted/temp-mounted VM 398/498. For live recovery, pseudo-disk driver 356 also interoperates with the cloud-based virtual disk(s) 404 assigned to the recovery VM (see
For live mount, pseudo-disk driver 356 configures the private store and recall store at recovery computing device 350 and pseudo-disk driver 356 intercepts read and write requests issued by live-mounted VM 398. Data in the write requests is stored to the private store. Ultimately, data in the recovery computing device-based private store will be discarded when live-mounted VM 398 expires or is taken down. Read requests issued by live-mounted VM 398 are intercepted by pseudo-disk driver 356 and are preferentially served from the private store or the recall store at recovery computing device 350; if a read request cannot be served therefrom, pseudo-disk driver 356 issues a read request to recall thread 354 to retrieve the data from a secondary copy, e.g., backup copy 116 or VM snapshot 316; stores the response to the recall store; and serves the read request from the recall store. In this way, the recall store is populated over time from on-demand read requests issued by the live-mounted VM 398. An example embodiment of pseudo-disk driver 356 is the “cvblk” driver developed by Commvault Systems, Inc. of Tinton Falls, N.J., USA. Another example use case of the pseudo-disk driver is described in U.S. Pat. No. 9,852,026 entitled “Efficient Application Recovery In An Information Management System Based On A Pseudo-Storage-Device Driver,” which is owned by the applicant and which is incorporated by reference in its entirety herein.
Pseudo-disk 358 is generated and managed by pseudo-disk driver 356 and comprises two logically defined storage areas configured at recovery computing device 350—the recall store and the private store.
Operating system (OS) mount point 370 is a mount point specially configured for accessing an OS virtual disk or OS partition of a virtual disk for live-mounted VM 398. Illustratively, mount point 370 is configured for NFS. Mount points are well known in the art.
Data disk mount point(s) 372 are one or more mount points specially configured for accessing data (not OS) virtual disks or partitions for live-mounted VM 398. Illustratively, mount point(s) 372 are configured for iSCSI.
Selected boot image 362 is one of the machine images in machine image pool 360 and is well known in the art. Boot image 362 is selected (e.g., by recovery process 352) to match or closely mimic the source VM from which VM backup copy 116 and/or VM snapshot 316 was created, so that the target VM 398 will be compatible with it when live-mounted.
A high level workflow of the illustrative live mount feature includes the following operations. At step A, recovery computing device 350 receives a request to live-mount a VM from storage manager 340. Illustratively, an OS disk flag is included in the request. Recovery computing device 350 invokes recovery process 352. At step B, recovery computing device 350 (e.g., using recovery process 352) invokes pseudo-disk driver 356, which creates a pseudo-disk block storage device 358 at recovery computing device 350. One pseudo-disk 358 is created for each virtual disk in the backup copy 116/VM snapshot 316. For simplicity, the rest of the workflow will refer to a single pseudo-disk 358. Recovery process 352 creates recall thread 354, which will become active after target VM 398 is powered on. At step C, mount (using OS disk mount point 370) all partitions of OS disk from backup copy 116/VM snapshot 316 and export over NFS. At step D, mount all data (non-OS) disks/partitions from backup copy 116/VM snapshot 316 using data disk mount point(s) 372; create a unique iSCSI Qualified Name (IQN) for each iSCSI target and create a unique IQN for iSCSI initiator for each request followed by creating a LUN under the IQN target to export cvblk data disk as SCSI disk. Based on this information, a user-data script is generated (e.g., by recovery process 352), which will be used at step F. At step E, recovery logic 352 selects boot image 362 for target VM 398. At step F, VM 398 is created from selected boot image 362 and includes the user-data script. The VM is booted up (for the first time) and the cloud-init routine executes the user-data script to customize VM 398. Cloud-init is used for customizing cloud-based images and is well known in the art. During this first boot, the user-data script detects that this is the first boot of the VM and updates kernel parameters for NFS root (per step B) and causes VM 398 to reboot (second boot) from NFS, i.e., booting off the OS partition/disk of the backup copy.
After the reboot, the user-data script detects it is a reboot and takes a different path than on first boot. Accordingly, the user-data script attaches VM 398 to the other (non-OS) partitions/disks that were exposed as iSCSI targets at step C. At this point, VM 398 sees all the mounted file systems (via mount points 370 and 371) and is now “live mounted.” Live-mounted VM 398 issues reads and writes, which are intercepted by pseudo-disk driver 356. Pseudo-disk driver 356 writes all data from writes into pseudo-disk 358 (at the private store) and attempts to serve reads to VM 398. If a read cannot be served from the private store or the recall store at the pseudo-disk, pseudo-disk driver 356 initiates a request to recall thread 354 to obtain the requested data (e.g., extent) from the secondary copy, e.g., backup copy 116, VM snapshot 316. At step G, recall thread 354 requests the extent from media agent 344, which locates it on backup media with the help of backup index 153 and retrieves the extent at step H. The extent is then delivered to recall thread 354 and transmitted to pseudo-disk driver 356. At step G′, recall thread 354 retrieves the extent from VM snapshot 316 using a cloud-native API for accessing cloud-based snapshots and delivers the extent to pseudo-disk driver 356. After step G/H or G′, pseudo-disk driver 356 stores the recalled extent to the recall store in pseudo-disk 358 and serves it from there to live-mounted VM 398. When live-mounted VM 398 expires or is powered off, pseudo-disk driver 356 discards pseudo-disk 358, including the private store that hosted new data issued by live-mounted VM 398. More details are given in
An illustrative logic for the user-data script used by VM 398 comprises: if nfsroot is not set, then claim iSCSI disks using initiator login and mount other partitions of OS disk; if nfsroot is not set, then replace set nfsroot as “NFS server recovery computing device IP:mount point of root filesystem” and on completion reboot the VM. The following entry makes the user-data script run again on each reboot:
In sum, VM live mount provides streamlined access to backed up VM data using a VM in a public cloud computing environment without having to restore entire backup copies. Rather, only data expressly requested by the live-mounted VM is recalled on demand from backup media. The backup copies are non-cloud-native and can be in any format generated by the backup system that created them, e.g., the illustrative data storage management system or another compatible system. In some embodiments, the backup copy is a cloud-native snapshot of a VM, which is also made accessible without having to restore it in its entirety. The solution works for Linux family guest OSs on the live-mounted VM because Linux OS supports booting over NFS. When Windows OS supports booting over NFS or CIFS, live mount can be implemented for Windows OS live-mounted VMs as well. Additional details are depicted in
Live Recovery of Virtual Machines in a Public Cloud Computing Environment
“Live Recovery” of a VM provides a longer-term platform than live mount because it generates a new “recovery VM” that operates as an ongoing “live” production platform. The recovery VM advantageously inherits new data written by another, temporarily-mounted, VM that operates while a VM backup copy or VM snapshot is restored in the background. The new data generated by the temp-mounted VM is captured in cloud-based virtual disk(s) that attach to the recovery VM as its datastore after the restore operation completes. Live recovery causes data blocks from a backup copy or snapshot to be restored (recovered, retrieved, recalled, moved) from backup media to cloud-based virtual disk(s) assigned to the recovery VM. The restore operation methodically transfers all data portions of the backup copy/snapshot to the cloud-based virtual disk(s), enabling the cloud-based recovery VM to be fully operational in the cloud computing environment on a going-forward basis. The advantage of live recovery over a traditional restore operation is that live recovery enables a cloud-based VM to begin operating sooner than waiting for the restore to complete. With live recovery, VM reads and writes begin issuing sooner, even while the restore operation is still in progress, and the writes are not lost on completion. However, the prior art does not support live recovery of a VM in a public cloud computing environment from a non-cloud-native backup copy, as explained in regard to the live mount feature. The present disclosure presents a technological solution that overcomes the deficiencies of the prior art.
Data storage management system 400 is analogous to system 100 and system 300 and is further enhanced with features and/or components for live recovering of VMs in a public cloud computing environment as described herein. Illustratively, system 400 comprises all depicted components in the present figure except for public cloud computing environment 301, but the invention is not so limited.
Recovery VM 402 is a virtual machine that executes in public cloud computing environment 301 and is the “final” endpoint of the illustrative live recovery feature described herein. Recovery VM 402 uses cloud-based virtual disk(s) 404 as its datastore.
Cloud-based virtual disk (vdisk) 404 is a virtual disk configured in public cloud computing environment 301. Vdisk 404 is initially used to host a pseudo-disk managed by a pseudo-disk driver and ultimately attaches to recovery VM 402 as its datastore, while retaining all the data previously added by another VM—temp-mounted VM 498.
Recovery computing device 450 is analogous to recovery computing device 350 and additionally comprises certain components shown in
Temp-mounted VM 498 is a cloud-based virtual machine configured in public cloud computing environment 301. This VM is mounted temporarily, typically while VM backup copy 116 or VM snapshot 316 is restored to vdisk 404, and provides “live” service to users based on the data source, i.e., VM backup copy 116 or VM snapshot 316. Like live-mounted VM 398, temp-mounted VM 498 is communicatively coupled to pseudo-disk driver 356, which intercepts its reads and writes. Unlike live-mounted VM 398, temp-mounted VM 498 has its reads and writes served by vdisk 404, which retains the writes issued by temp-mounted VM 498 and ultimately becomes the datastore for recovery VM 402. At an appropriate time after the restore to vdisk 404 is finished, temp-mounted VM 498 is powered down and control switches over to recovery VM 402. More details are given in
Recovery process 352 illustratively comprises recall thread 354 and data mover thread 455 and in some embodiments also comprises pseudo-disk driver 356, but the invention is not so limited. Recovery process 352 coordinates some aspects of the live recovery operation, such as invoking a pseudo-disk driver 356 corresponding to each VM virtual disk of the source VM, causing the pseudo-disk driver 356 to set up a corresponding pseudo-disk at vdisk 404, selecting a suitable machine image 362 from machine image pool 360, causing VM 498 to be instantiated for the temp-mount operation, and also acts to coordinate the switchover from temp-mounted VM 498 to recovery VM 403. In some embodiments recovery process 352 acts as NFS server and iSCSI target to export OS and data disks which use mount points 370 and 372, respectively.
Data mover thread 455 is an illustrative thread that runs within recovery process 352. Data mover thread 455 causes the VM backup copy 116 or VM snapshot 316 to be sequentially traversed to move data extents directly to vdisk 404. Data mover thread 455 sequentially requests the next extent from pseudo-disk-driver 356, which invokes recall thread 354 to recover the extent from VM backup copy 116 or VM snapshot 316. Data mover thread 455 ensures synchronization and arbitration in cases where an on-demand read arrives for an extent that is currently being recalled from backup copy/snapshot. In such a case, data mover thread 455 allows the recall to complete and then the on-demand read request is served from vdisk 404. This logic ensures that there are no double-takes on recalls from backup copy/snapshot.
I/O bitmap 456 is a data structure configured and maintained at recovery computing device 450. Illustratively, bitmap 456 is maintained by pseudo-disk driver 356 when running the live recovery feature. Bitmap 456 tracks whether each extent of the data source (e.g., VM backup copy 116, VM snapshot 316) has been recalled and/or written by temp-mounted VM 498. This process ensures that all of the data source is recovered and its extents stored to the cloud-based virtual disk(s) 404.
Pseudo-disk driver 356 executes on recovery computing device 450. The illustrative pseudo-disk driver 356 interoperates with the media agent 344 and with temp-mounted VM 498, as well as with cloud-based virtual disk(s) 404 assigned to recovery VM 402. pseudo-disk driver 356. In contrast to live mount, in the live recovery feature pseudo-disk driver 356 configures its pseudo-disk, including private store and recall store, at cloud-based vdisk 404 that will ultimately serve as the recovery VM's datastore. This aspect is distinguishable from the expendable pseudo-disk 358 created for the live mount feature at recovery computing device 350. This key aspect provides a streamlined transfer of data from the data source to the final destination, i.e., to the recovery VM's datastore in the cloud.
A high level workflow of the illustrative live recovery feature includes the following operations. At step A, recovery computing device 450 receives a request to live recover a VM from storage manager 340. Illustratively, an OS disk flag is included in the request. Recovery computing device 450 invokes recovery process 352. At step B′, recovery computing device 450 (e.g., using recovery process 352) instantiates cloud-based vdisk(s) 404 and invokes pseudo-disk driver 356, which creates a pseudo-disk block storage device at vdisk(s) 404. One pseudo-disk is created for each virtual disk in the VM backup copy 116/VM snapshot 316. For simplicity, the rest of the workflow will refer to a single pseudo-disk and/or single vdisk 404. Recovery process 352 creates recall thread 354, which will become active after VM 498 is powered on. At step BB, recovery process 352 creates data mover thread 455 and instantiates I/O bitmap 456. Data mover thread 455 begins to sequentially request data extents from pseudo-disk driver 356, which in turn requests the data extents from recall thread 354 followed by steps G/H or G′. As the extents are recalled from the data source and placed in the recall store, pseudo-disk driver 356 updates bitmap 456 to reflect this.
At step C, mount (using OS disk mount point 370) all partitions of OS disk from backup copy 116/VM snapshot 316 and export over NFS. At step D, mount all data (non-OS) disks/partitions from backup copy 116/VM snapshot 316 using data disk mount point(s) 372; create a unique iSCSI Qualified Name (IQN) for each iSCSI target and create a unique IQN for iSCSI initiator for each request followed by creating a LUN under the IQN target to export cvblk data disk as SCSI disk. Based on this information, a user-data script is generated (e.g., by recovery process 352), which will be used at step F. An illustrative logic for the user-data script used by VM 498 is given in the text accompanying
After the reboot, the user-data script detects it is a reboot and takes a different path than on first boot. Accordingly, the user-data script attaches VM 498 to the other (non-OS) partitions/disks that were exposed as iSCSI targets at step C. At this point, VM 498 sees all the mounted file systems (via mount points 370 and 372) and is now “temp-mounted” to recovery computing device 450. Temp-mounted VM 498 issues reads and writes, which are intercepted by pseudo-disk driver 356. Pseudo-disk driver 356 writes all data from writes into the private store at vdisk 404 and attempts to serve reads to VM 498. If a read cannot be served from the private store or the recall store at vdisk 404, pseudo-disk driver 356 initiates a request to recall thread 354 to obtain the requested data (e.g., extent) from the data source, e.g., VM backup copy 116, VM snapshot 316. At step G, recall thread 354 requests the extent from media agent 344, which locates it on backup media with the help of backup index 153 and retrieves the extent at step H. The extent is then delivered to recall thread 354 and transmitted to pseudo-disk driver 356. At step G′, recall thread 354 retrieves the extent from VM snapshot 316 using a cloud-native API for accessing cloud-based snapshots and delivers the extent to pseudo-disk driver 356. After step G/H or G′, pseudo-disk driver 356 stores the recalled extent to the recall store in the pseudo-disk at vdisk 404 and serves it from there to temp-mounted VM 498 in response to a read request from temp-mounted VM 498.
At step I, data mover thread 455 detects that the data source traversal is complete and indicates “end of move” to recovery process 352. At this point, system 400 is ready to switch over from temp-mounted VM 498 to recovery VM 402. Accordingly, all connections between recovery computing device 450 and vdisk 404 are severed. At step J, recovery VM 402 is created at public cloud computing environment 301, vdisk 404 is attached thereto as its datastore, and recovery VM 402 is powered on and able to provide live VM service. All data written by temp-mounted VM 498 is still on vdisk 404 and available to recovery VM 402, thus assuring continuity of service. More details are given in
In sum, VM live recovery provides a new working VM (the recovery VM) in the public cloud with minimal downtime and without waiting to fully restore backup copies. The feature also enables migration of VMs across public clouds or from non-cloud (on-prem) data centers to public cloud. As noted, embodiments are contemplated for non-cloud-to-cloud, cloud-to-cloud, and multi-cloud live recovery, with the recovery VM operating in a public cloud computing environment. See, e.g.,
At block 702, initiate live mount of VM in a public cloud computing environment from a data source, e.g., non-cloud-native VM backup copy 116, VM snapshot 316. Typically, a user might initiate a request to live mount a certain VM backup copy or VM snapshot, thus causing storage manager 340 to initiate the live mount feature within system 300, which roughly corresponds to step A in
At block 704, pseudo-disk driver creates a pseudo-disk at recovery computing device 350 for each virtual disk in the VM backup copy and/or VM snapshot. This roughly corresponds to step B in
At block 706, mount root partition of boot disk of the data source (VM backup copy or VM snapshot) & export over NFS. This roughly corresponds to step C in
At block 708, expose other (non-OS) partitions/disks as iSCSI targets. This roughly corresponds to step D in
At block 710, generate the user-data script for VM 398. Illustratively, recovery process 352 generates the user-data script based on information gathered at blocks 706-708 (steps C, D in
At block 712, cloud-based VM 398 is created, booted, and re-booted. Upon completion of the re-boot, VM 398 is able to issue reads and writes. This block rough corresponds to steps E and F in
At block 714, pseudo-disk driver 356 intercepts reads and writes issued by VM 398. For writes, control passes to block 720 and for reads control passes to block 716/718.
At block 716/718, which is reached when pseudo-disk driver 356 receives a read request from VM 398, if the requested extent is not found in the pseudo-disk (i.e., in private store or in recall store)—meaning this extent has not been written or read by VM 398 during the present live mount operation—pseudo-disk driver 356 invokes recall thread 354 on demand to read the extent from the VM backup copy 116 (block 716) or from VM snapshot 316 (block 718).
At block 720, which is reached when VM 398 issues a write, pseudo-disk driver 356 saves the data in the write into the pseudo-disk, e.g., to the private store.
At block 722, pseudo-disk driver 356 serves reads from the pseudo-disk, first checking the private store and then the recall store for extents that have been previously written or read, respectively, by VM 398.
At block 724, which occurs after live-mounted VM 398 expires or is powered off on demand, the pseudo-disk is discarded, e.g., by recovery process 352. This means that writes saved to the pseudo-disk private store from live-mounted VM 398 also are discarded. The present live-mount operation ends here.
It should be noted that system 300 may run multiple live mount operations at any given time using a corresponding plurality of live-mounted VMs 398. In some embodiments, multiple live-mounted VMs may run off the same data source (e.g., VM backup copy 116, VM snapshot 316), each live-mounted VM interoperating with a distinct pseudo-disk driver 356 and corresponding pseudo-disk(s) 358. Each live-mounted VM has its own duration and lifecycle distinct from the duration/lifecycle of other live-mounted VMs in system 300, whether they use the same or different data source (e.g., VM backup copy 116, VM snapshot 316).
At block 7122, recovery process 352 select machine image 362 from machine image pool 360. The selected machine image is suitable for cloud-based VM 398, e.g., based on operating system type and version, and/or other VM attributes. This roughly corresponds to step E in
At block 802, initiate live recovery of a VM in a public cloud computing environment from a data source, e.g., non-cloud-native VM backup copy 116, VM snapshot 316. Typically, a user might initiate a request to live recover or migrate a certain VM backup copy or VM snapshot at a cloud destination, thus causing storage manager 340 to initiate the live recovery feature within system 400, which roughly corresponds to step A in
At block 804, pseudo-disk driver 356 creates a pseudo-disk at a cloud-native data storage volume supplied by public cloud computing environment 301, the cloud-based vdisk 404 (cloud-based vdisk). This roughly corresponds to step B′ in
At block 806, data mover thread 455 sequentially requests the next extent from pseudo-disk driver 356, which invokes recall thread 354 for recovery from the data source, e.g., VM backup copy 116, VM snapshot 316. Pseudo-disk driver sets up I/O bitmap 456. This roughly corresponds to step BB in
At block 808, pseudo-disk driver 356 saves restored extents to the recall store of pseudo-disk(s) in cloud-based vdisk 404, unless overwritten by writes issued by VM 498. Pseudo-disk driver 356 updates bitmap 456 to reflect the successful recall.
At block 810, VM 498 is temporarily mounted in public cloud computing environment 301. This operation corresponds to blocks 710-712 in method 700 and will not be reiterated here. See also steps E and F in
At block 812, pseudo-disk driver 356 intercepts reads and writes issued by VM 498, as described in block 714 of method 700.
At block 814, if extent not found in pseudo-disk recall store or private store, pseudo-disk driver 356 invokes recall thread 354 on demand to read from the data source as described in more detail at blocks 716/718 of method 700.
At block 816, pseudo-disk driver 356 saves writes issued by VM 498 to the private store of pseudo-disk(s) in cloud-based vdisk 404, as described in more detail at block 720 of method 700.
At block 818, bitmap 456 tracks which extents have been recovered from the data source and/or written by VM 498. Some extents might have been recovered from the data source based on a read request issued by VM 498 while others were recovered through the sequential requests issued by the data mover thread 455.
At block 820, pseudo-disk driver 356 serves reads to VM 498 from pseudo-disk(s) in cloud-based vdisk 404, preferentially searching the private store, then the recall store.
At block 822, the live recovery feature switches over from temp-mounted VM 498 to recovery VM 402. Recovery VM 402 acquires access to data in cloud-based virtual disk(s) 404, including data written by temp-mounted VM 498 prior to the switchover. More details are given in
It should be noted that system 400 may run multiple live recovery operations at any given time using a plurality of VMs 498 and corresponding recovery VMs 402. In some embodiments, multiple live-mounted VMs 398 may run off the same data source (e.g., VM backup copy 116, VM snapshot 316) as a temp-mounted VM 498, each VM 398/498 interoperating with a distinct pseudo-disk driver 356 and corresponding pseudo-disk(s) suitably configured.
At block 8222, When all extents have been read from the data source (e.g., VM backup copy 116, VM snapshot 316), data mover thread 455 detects end of file at the data source's storage media. Accordingly, data mover thread 455 indicates “End of Move” to recovery process 352 and/or to pseudo-disk driver 356.
At block 8223, based on the “end of move” indication, system 400 is now ready to switch over to cloud-based recovery VM 402. The readiness status may be communicated by recovery process 352 to storage manager 340 in some embodiments.
At block 8224, which is a decision point, system 400 determines whether the switchover should be user-Initiated or automatic. If automatic, control passes to block 8226; for user-initiated switchover control passes to block 8225.
At block 8225, which is reached when users control switchover initiation, system 400 keeps using temp-mounted VM 498 (adding new writes to cloud-based vdisk 404) until a user triggers switchover. The user may be the same one as the initiator of the live recovery operation at block 802, or may be a system administrator, without limitation.
At block 8226, the switchover from temp-mounted VM 498 to recovery VM 402 is triggered and a short period of downtime begins with block 8227.
At block 8227, system 400 (e.g., recovery process 352) powers off temp-mounted VM 498; creates cloud-based recovery VM 402; attaches cloud-based vdisk 404 to recovery VM 402 as its datastore; and powers up recovery VM 402. Before powering off VM 498, any applications that run thereon may be quiesced in order to stop writing new data into vdisk 404.
At block 8228, after recovery VM 42 is powered up, the switchover is complete and the temporary downtime ends. Recovery VM 402 is operational with recovered backup data from the data source (e.g., VM backup copy 116, VM snapshot 316) plus new data written by temp-mounted VM 498.
At block 8229, preferably after recovery VM 492 is operational, system 400 (e.g., recovery process 352) detaches the pseudo-disk(s) from temp-mounted VM 498; and detaches cloud-based volumes hosting vdisk 404 from recovery computing device 450. Block 822 ends here.
In regard to the present disclosure, other embodiments are possible, such that the above-recited components, workflows, steps, blocks, operations, messages, requests, queries, and/or instructions are differently arranged, sequenced, sub-divided, organized, and/or combined. In some embodiments, a different component of system 300/400 may initiate or execute a given operation.
Some example enumerated embodiments of the present invention are recited in this section in the form of methods, systems, and non-transitory computer-readable media, without limitation.
According to an example embodiment, a computer-implemented method for recovering a virtual machine in a cloud computing environment operated by a cloud service provider comprises: activating a first computing device in the cloud computing environment, wherein the first computing device executes a pseudo-disk driver; by the first computing device operating in the cloud computing environment: for a virtual disk of a first virtual machine backed up in a backup copy, configuring a corresponding virtual disk in the cloud computing environment, wherein the backup copy was generated by a data storage management system distinct from the cloud computing environment. The above-recited embodiment further comprising: sequentially requesting from the pseudo-disk driver a next data extent from the backup copy; by the pseudo-disk driver, responsive to the sequentially requesting, causing a media agent to restore each requested data extent from the backup copy to the virtual disk in the cloud computing environment, and tracking which data extents of the backup copy have been restored from the backup copy. The above-recited embodiment further comprising: concurrently with the sequentially requesting, activating a third virtual machine in the cloud computing environment, wherein the third virtual machine boots off a pre-defined machine image selected from a pool of machine images in the cloud computing environment, wherein a user-data script (i) customizes the third virtual machine to use a first mount point at the first computing device exported over Network File System (NFS) protocol for booting off a root partition of a boot disk of the backup copy and (ii) causes the third virtual machine to reboot, and wherein the third virtual machine reboots over the NFS protocol via the first mount point and attaches to other disks of the backup copy exposed as Internet Small Computer Systems Interface (iSCSI) targets via a second mount point at the first computing device. The above-recited embodiment further comprising: concurrently with the sequentially requesting, intercepting read requests and write commands issued by the rebooted third virtual machine and writing data received in the write commands to the virtual disk in the cloud computing environment. The above-recited embodiment further comprising: after all data extents of the backup copy are restored to the virtual disk in the cloud computing environment, causing the third virtual machine to be powered off and attaching the virtual disk in the cloud computing environment to a second virtual machine in the cloud computing environment. The above-recited embodiment further comprising: powering on the second virtual machine in the cloud computing environment, wherein the powered on second virtual machine accesses data in the virtual disk created in the cloud computing environment, including data written by the third virtual machine while attached thereto.
The above-recited embodiment wherein the media agent executes on one of: the first computing device, a second computing device distinct from the first computing device and operating in the cloud computing environment, and a second computing device distinct from the first computing device and operating outside the cloud computing environment; and wherein the media agent is communicatively coupled to one or more data storage devices that store the backup copy of the first virtual machine. The above-recited embodiment further comprising: by a storage manager that manages storage operations in the data storage management system and executes on a second computing device distinct from the first computing device, instructing the first computing device to initiate a live recovery of the backup copy of the first virtual machine to the second virtual wherein the instructing includes an identifier for the root partition of the boot disk of the first virtual machine as backed up in the backup copy. The above-recited embodiment wherein the tracking stores information in a bitmap data structure that corresponds to a plurality of data extents of the backup copy. The above-recited embodiment further comprising: by the first computing device operating in the cloud computing environment: detaching the virtual disk in the cloud computing environment from the first computing device after the second virtual machine is powered on. The above-recited embodiment further comprising: configuring the pool of machine images in the cloud computing environment, wherein each machine image comprises an operating system image and one or more drivers needed by the third virtual machine to interoperate with the first computing device. The above-recited embodiment wherein a cloud-init routine that executes at the third virtual machine when the third virtual machine boots off the selected machine image executes the user-data script to customize the third virtual machine. The above-recited embodiment wherein the rebooted third virtual machine provides virtual machine service to a user sooner than restoring all of the backup copy of the first virtual machine to the second virtual machine without using the rebooted third virtual machine. The above-recited embodiment wherein powering on the second virtual machine with access to data in the virtual disks in the cloud computing environment, including data written by the third virtual machine while attached to the virtual disks, completes a migration of the first virtual machine to the cloud computing environment from one of: outside the cloud computing environment, another availability zone of the cloud computing environment, and another service account of the cloud computing environment.
According to another example embodiment, a computer-implemented method for recovering a virtual machine snapshot in a cloud computing environment operated by a cloud service provider, the method comprises: by a pseudo-disk driver that executes on a first computing device in the cloud computing environment, for each virtual disk in a snapshot of a first virtual machine, creating a corresponding virtual disk configured in a cloud-native data storage volume supplied by the cloud computing environment, and wherein the snapshot is cloud-native to the cloud computing environment; by a data mover thread that executes on the first computing device, sequentially requesting from the pseudo-disk driver a next data extent from the snapshot; by the pseudo-disk driver, responsive to the data mover thread, invoking a recall thread at the first computing device that recalls each requested data extent from the snapshot to the virtual disks in the cloud computing environment, and tracking which data extents of the snapshot have been recalled from the snapshot. The above-recited embodiment further comprising: concurrently with the data mover thread, causing by the first computing device a third virtual machine to be activated in the cloud computing environment, wherein the third virtual machine boots off a pre-defined machine image selected from a pool of machine images in the cloud computing environment, wherein a user-data script (i) customizes the third virtual machine to use a first mount point at the first computing device exported over Network File System (NFS) protocol for booting off a root partition of a boot disk of the snapshot and (ii) causes the third virtual machine to reboot. The above-recited embodiment wherein the third virtual machine reboots over the NFS protocol via the first mount point and attaches to other disks of the snapshot exposed as Internet Small Computer Systems Interface (iSCSI) targets via a second mount point at the first computing device. The above-recited embodiment further comprising: concurrently with the data mover thread and after the reboot of the third virtual machine, by the pseudo-disk driver, intercepting read requests and write commands issued by the third virtual machine, wherein the pseudo-disk driver writes data received in the write commands to the virtual disks in the cloud computing environment. The above-recited embodiment further comprising: concurrently with the data mover thread and after the reboot of the third virtual machine, by the pseudo-disk driver, serving the read requests from the virtual disks in the cloud computing environment, and if first data requested in a read request received from the third virtual machine is not available in the virtual disks, invoking the recall thread on demand to recall the first data from the snapshot to the virtual disks in the cloud computing environment. The above-recited embodiment further comprising: by the first computing device, after all data extents of the snapshot are recalled to the virtual disks, causing the third virtual machine to be powered off and attaching the virtual disks to a second virtual machine in the cloud computing environment. The above-recited embodiment further comprising: powering on the second virtual machine in the cloud computing environment, wherein the second virtual machine accesses data in the virtual disks created in the cloud computing environment, including data written by the third virtual machine while attached to the virtual disks.
The above-recited embodiment wherein the recall thread at the first computing device recalls data from the snapshot of the first virtual machine using an application programming interface (API). The above-recited embodiment further comprising: by a storage manager that manages storage operations in the data storage management system and executes on a second computing device distinct from the first computing device, instructing the first computing device to initiate a live recovery of the snapshot of the first virtual machine to the second virtual machine in the cloud computing environment. The above-recited embodiment wherein the instructing includes an identifier for the root partition of the boot disk of the first virtual machine as captured in the snapshot. The above-recited embodiment wherein the tracking stores information in a bitmap data structure that corresponds to a plurality of data extents of the snapshot. The above-recited embodiment wherein the pseudo-disk driver maintains a pseudo-disk at each of the virtual disks in the cloud computing environment, and wherein each pseudo-disk corresponds to a virtual disk of the first virtual machine captured in the snapshot. The above-recited embodiment further comprising: configuring the pool of machine images in the cloud computing environment, wherein each machine image comprises an operating system image and one or more drivers needed by the third virtual machine to interoperate with the first computing device. The above-recited embodiment wherein a cloud-init routine that executes at the third virtual machine when the third virtual machine boots off the selected machine image executes the user-data script to customize the third virtual machine. The above-recited embodiment wherein the rebooted third virtual machine provides virtual machine service to a user sooner than restoring all of the snapshot of the first virtual machine to the second virtual machine without using the rebooted third virtual machine. The above-recited embodiment wherein powering on the second virtual machine with access to data in the virtual disks in the cloud computing environment, including data written by the third virtual machine while attached to the virtual disks, completes a migration of the first virtual machine to the cloud computing environment from one of: outside the cloud computing environment, another availability zone of the cloud computing environment, and another service account of the cloud computing environment.
According to an illustrative embodiment, a data storage management system for live-mounting a non-cloud-native backup copy of a virtual machine in a cloud computing environment comprises: a first computing device operating in the cloud computing environment, wherein the first computing device is configured to: create a plurality of pseudo-disks at the first computing device, wherein each of the plurality of pseudo-disks corresponds to a virtual disk backed up in a backup copy of a first virtual machine, wherein the backup copy is not cloud-native to the cloud computing environment, and wherein each of the plurality of pseudo-disks presents as a block storage device at the first computing device; select, from a pool of pre-defined machine images configured in the cloud computing environment, a first machine image suitable for live-mounting the first virtual machine; cause a second virtual machine to boot in the cloud computing environment off the first machine image selected from the pool. The above-recited embodiment wherein a cloud-init routine, which executes at the second virtual machine when the second virtual machine boots, executes a user-data script that (i) customizes the second virtual machine to use a first mount point at the first computing device exported over Network File System (NFS) protocol for booting off a root partition of a boot disk of the backup copy, and (ii) causes the second virtual machine to reboot. The above-recited embodiment wherein the second virtual machine reboots over the NFS protocol via the first mount point and attaches to other disks of the backup copy exposed as Internet Small Computer Systems Interface (iSCSI) targets via a second mount point at the first computing device. The above-recited embodiment wherein the first computing device is further configured to: intercept read requests and write commands issued by the second virtual machine after the reboot. The above-recited embodiment wherein the first computing device is further configured to: write data received in the write commands to the plurality of pseudo-disks. The above-recited embodiment wherein the first computing device is further configured to: on receiving a first read request, serve the first read request from the plurality of pseudo-disks, and if first data requested in the first read request is not found the plurality of pseudo-disks, cause a media agent to restore the first data from the backup copy to one of the plurality of pseudo-disks at the first computing device before serving the first read request therefrom. The above-recited embodiment wherein after the reboot, the second virtual machine provides live-mounted virtual machine service in the cloud computing environment, with on-demand access to backed up data of the first virtual machine from the backup copy and without restoring all of the backup copy to the cloud computing environment. The above-recited embodiment wherein the first computing device is further configured to: after powering down the second virtual machine, discard the plurality of pseudo-disks including the data received in the write commands. The above-recited embodiment wherein after the reboot, the second virtual machine provides live-mounted virtual machine service in the cloud computing environment, using the plurality of pseudo-disks at the first computing device, with on-demand access to backed up data of the first virtual machine from the backup copy and without restoring all of the backup copy to the cloud computing environment.
The above-recited embodiment wherein after the reboot, the second virtual machine provides virtual machine service in the cloud computing environment sooner than restoring all of the backup copy of the first virtual machine to the cloud computing environment. The above-recited embodiment wherein the second virtual machine accesses data of the root partition of the boot disk of the first virtual machine, backed up in the backup copy, via the first mount point over NFS protocol. The above-recited embodiment wherein the second virtual machine accesses non-root data of the backup copy via one or more iSCSI targets. The above-recited embodiment further comprising the media agent, wherein the media agent executes on one of: the first computing device, a second computing device distinct from the first computing device and operating in the cloud computing environment, and a second computing device distinct from the first computing device and operating outside the cloud computing environment; and wherein the media agent is communicatively coupled to one or more data storage devices where the backup copy is stored. The above-recited embodiment further comprising: a storage manager that manages storage operations in the data storage management system and executes on a second computing device distinct from the first computing device; and wherein the storage manager is configured to instruct the first computing device to live-mount a virtual machine from the backup copy of the first virtual machine to the second virtual machine in the cloud computing environment. The above-recited embodiment wherein instructing the first computing device to live-mount includes an identifier for the root partition of the boot disk of the first virtual machine as backed up in the backup copy. The above-recited embodiment wherein the first computing device executes a pseudo-disk driver that maintains the plurality of pseudo-disks created for the backup copy, and wherein the pseudo-disk driver intercepts the read requests and write commands issued by the second virtual machine. The above-recited embodiment wherein each pre-defined machine image in the pool comprises an operating system image and one or more drivers needed by the second virtual machine to interoperate with the first computing device, and wherein the machine image pool is configured in the cloud computing environment. The above-recited embodiment wherein the backup copy is stored on one or more data storage devices at one of: within the cloud computing environment, outside the cloud computing environment, within another availability zone of the cloud computing environment, and within another service account of the cloud computing environment.
According to another example embodiment, a computer-implemented method for live-mounting a cloud-native snapshot of a virtual machine in a cloud computing environment comprises: by a first computing device operating in a cloud computing environment: by a pseudo-disk driver that executes on the first computing device, creating a plurality of pseudo-disks at the first computing device, wherein each of the plurality of pseudo-disks presents as a block storage device at the first computing device, wherein each of the plurality of pseudo-disks corresponds to a virtual disk backed up in a snapshot of a first virtual machine, and wherein the snapshot is cloud-native to and is stored in the cloud computing environment; selecting, from a pool of pre-defined machine images configured in the cloud computing environment, a first machine image corresponding to the first virtual machine; causing a second virtual machine to boot in the cloud computing environment off the first machine image selected from the pool, wherein a user-data script customizes the second virtual machine and causes the second virtual machine to reboot, and wherein after the reboot the second virtual machine issues read requests and write commands. The above-recited embodiment further comprising: by the pseudo-disk driver, intercepting the read requests and the write commands issued by the second virtual machine after the reboot. The above-recited embodiment further comprising: by the pseudo-disk driver, writing data received in the write commands to the plurality of pseudo-disks. The above-recited embodiment further comprising: by the pseudo-disk driver, on receiving a first read request, serving the first read request from the plurality of pseudo-disks, and if first data requested in the first read request is not found in any of the plurality of pseudo-disks, recalling the first data from the snapshot to one of the plurality of pseudo-disks at the first computing device before serving the first read request to the second virtual machine therefrom. The above-recited embodiment wherein after the reboot, the second virtual machine, in conjunction with the first computing device, provides live-mounted virtual machine service in the cloud computing environment, with on-demand access to data of the first virtual machine from the snapshot and without restoring all of the snapshot to the second virtual machine. The above-recited embodiment further comprising: after powering down the second virtual machine, discarding the plurality of pseudo-disks including the data received in the write commands. The above-recited embodiment further comprising: wherein after the reboot, the second virtual machine provides live-mounted virtual machine service in the cloud computing environment, using the plurality of pseudo-disks at the first computing device, with on-demand access to backed up data of the first virtual machine from the snapshot and without restoring all of the snapshot to the second virtual machine.
The above-recited embodiment wherein the user-data script customizes the second virtual machine (i) to use a first mount point at the first computing device exported over Network File System (NFS) protocol for booting off a root partition of a boot disk of the backup copy, and (ii) to use one or more Internet Small Computer Systems Interface (iSCSI) targets exposed by the first computing device for accessing non-root data of the backup copy; and wherein after the reboot, the second virtual machine issues read requests and write commands to one or more of: the first mount point over NFS protocol and one of one or more iSCSI targets over iSCSI protocol. The above-recited embodiment wherein the rebooted second virtual machine accesses data of the root partition of the boot disk of the first virtual machine via the first mount point over NFS protocol. The above-recited wherein the rebooted second virtual machine accesses non-root data of the backup copy and writes non-root data via one or more iSCSI targets. The above-recited embodiment wherein the first computing device recalls data from the snapshot of the first virtual machine using an application programming interface (API). The above-recited embodiment further comprising: by a storage manager that manages storage operations in the data storage management system and executes on a second computing device distinct from the first computing device, instructing the first computing device to initiate a live-mount of a virtual machine from the snapshot of the first virtual machine to the second virtual machine in the cloud computing environment. The above-recited embodiment wherein instructing the first computing device includes an identifier for the root partition of the boot disk of the first virtual machine as captured in the snapshot of the first virtual machine. The above-recited embodiment wherein second computing device operates in one of: within the cloud computing environment, outside the cloud computing environment, another availability zone of the cloud computing environment, and another service account of the cloud computing environment. The above-recited embodiment further comprising: configuring the pool of machine images in the cloud computing environment, wherein each pre-defined machine image in the pool comprises an operating system image and one or more drivers needed by the second virtual machine to interoperate with the first computing device. The above-recited embodiment wherein a cloud-init routine, which executes at the second virtual machine when the second virtual machine boots off the selected machine image, executes the user-data script to customize the second virtual machine. The above-recited embodiment wherein the rebooted second virtual machine provides virtual machine service to a user sooner than restoring all of the snapshot of the first virtual machine to the second virtual machine.
In other embodiments, a system or systems operates according to one or more of the methods and/or computer-readable media recited in the preceding paragraphs. In yet other embodiments, a method or methods operates according to one or more of the systems and/or computer-readable media recited in the preceding paragraphs. In yet more embodiments, a non-transitory computer-readable medium or media causes one or more computing devices having one or more processors and computer-readable memory to operate according to one or more of the systems and/or methods recited in the preceding paragraphs.
Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment.
Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense, i.e., in the sense of “including, but not limited to.” As used herein, the terms “connected,” “coupled,” or any variant thereof means any connection or coupling, either direct or indirect, between two or more elements; the coupling or connection between the elements can be physical, logical, or a combination thereof. Additionally, the words “herein,” “above,” “below,” and words of similar import, when used in this application, refer to this application as a whole and not to any particular portions of this application. Where the context permits, words using the singular or plural number may also include the plural or singular number respectively. The word “or” in reference to a list of two or more items, covers all of the following interpretations of the word: any one of the items in the list, all of the items in the list, and any combination of the items in the list. Likewise the term “and/or” in reference to a list of two or more items, covers all of the following interpretations of the word: any one of the items in the list, all of the items in the list, and any combination of the items in the list.
In some embodiments, certain operations, acts, events, or functions of any of the algorithms described herein can be performed in a different sequence, can be added, merged, or left out altogether (e.g., not all are necessary for the practice of the algorithms). In certain embodiments, operations, acts, functions, or events can be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors or processor cores or on other parallel architectures, rather than sequentially.
Systems and modules described herein may comprise software, firmware, hardware, or any combination(s) of software, firmware, or hardware suitable for the purposes described. Software and other modules may reside and execute on servers, workstations, personal computers, computerized tablets, PDAs, and other computing devices suitable for the purposes described herein. Software and other modules may be accessible via local computer memory, via a network, via a browser, or via other means suitable for the purposes described herein. Data structures described herein may comprise computer files, variables, programming arrays, programming structures, or any electronic information storage schemes or methods, or any combinations thereof, suitable for the purposes described herein. User interface elements described herein may comprise elements from graphical user interfaces, interactive voice response, command line interfaces, and other suitable interfaces.
Further, processing of the various components of the illustrated systems can be distributed across multiple machines, networks, and other computing resources. Two or more components of a system can be combined into fewer components. Various components of the illustrated systems can be implemented in one or more virtual machines, rather than in dedicated computer hardware systems and/or computing devices. Likewise, the data repositories shown can represent physical and/or logical data storage, including, e.g., storage area networks or other distributed storage systems. Moreover, in some embodiments the connections between the components shown represent possible paths of data flow, rather than actual connections between hardware. While some examples of possible connections are shown, any of the subset of the components shown can communicate with any other subset of components in various implementations.
Embodiments are also described above with reference to flow chart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products. Each block of the flow chart illustrations and/or block diagrams, and combinations of blocks in the flow chart illustrations and/or block diagrams, may be implemented by computer program instructions. Such instructions may be provided to a processor of a general purpose computer, special purpose computer, specially-equipped computer (e.g., comprising a high-performance database server, a graphics subsystem, etc.) or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor(s) of the computer or other programmable data processing apparatus, create means for implementing the acts specified in the flow chart and/or block diagram block or blocks. These computer program instructions may also be stored in a non-transitory computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the acts specified in the flow chart and/or block diagram block or blocks. The computer program instructions may also be loaded to a computing device or other programmable data processing apparatus to cause operations to be performed on the computing device or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computing device or other programmable apparatus provide steps for implementing the acts specified in the flow chart and/or block diagram block or blocks.
Any patents and applications and other references noted above, including any that may be listed in accompanying filing papers, are incorporated herein by reference. Aspects of the invention can be modified, if necessary, to employ the systems, functions, and concepts of the various references described above to provide yet further implementations of the invention. These and other changes can be made to the invention in light of the above Detailed Description. While the above description describes certain examples of the invention, and describes the best mode contemplated, no matter how detailed the above appears in text, the invention can be practiced in many ways. Details of the system may vary considerably in its specific implementation, while still being encompassed by the invention disclosed herein. As noted above, particular terminology used when describing certain features or aspects of the invention should not be taken to imply that the terminology is being redefined herein to be restricted to any specific characteristics, features, or aspects of the invention with which that terminology is associated. In general, the terms used in the following claims should not be construed to limit the invention to the specific examples disclosed in the specification, unless the above Detailed Description section explicitly defines such terms. Accordingly, the actual scope of the invention encompasses not only the disclosed examples, but also all equivalent ways of practicing or implementing the invention under the claims.
To reduce the number of claims, certain aspects of the invention are presented below in certain claim forms, but the applicant contemplates other aspects of the invention in any number of claim forms. For example, while only one aspect of the invention is recited as a means-plus-function claim under 35 U.S.C. sec. 112(f) (AIA), other aspects may likewise be embodied as a means-plus-function claim, or in other forms, such as being embodied in a computer-readable medium. Any claims intended to be treated under 35 U.S.C. § 112(f) will begin with the words “means for,” but use of the term “for” in any other context is not intended to invoke treatment under 35 U.S.C. § 112(f). Accordingly, the applicant reserves the right to pursue additional claims after filing this application, in either this application or in a continuing application.
This application claims priority to U.S. Provisional Pat. App. No. 63/025,758 filed on May 15, 2020 with the title of “Live Mount And Live Recovery Of Virtual Machines In A Public Cloud Computing Environment.” Any and all applications for which a foreign or domestic priority claim is identified in the Application Data Sheet of the present application are hereby incorporated by reference in their entireties under 37 CFR 1.57.
Number | Name | Date | Kind |
---|---|---|---|
4084231 | Capozzi et al. | Apr 1978 | A |
4267568 | Dechant et al. | May 1981 | A |
4283787 | Chambers | Aug 1981 | A |
4417321 | Chang et al. | Nov 1983 | A |
4641274 | Swank | Feb 1987 | A |
4654819 | Stiffler et al. | Mar 1987 | A |
4686620 | Ng | Aug 1987 | A |
4912637 | Sheedy et al. | Mar 1990 | A |
4995035 | Cole et al. | Feb 1991 | A |
5005122 | Griffin et al. | Apr 1991 | A |
5093912 | Dong et al. | Mar 1992 | A |
5123107 | Mensch, Jr. | Jun 1992 | A |
5133065 | Cheffetz et al. | Jul 1992 | A |
5193154 | Kitajima et al. | Mar 1993 | A |
5212772 | Masters | May 1993 | A |
5226157 | Nakano et al. | Jul 1993 | A |
5239647 | Anglin et al. | Aug 1993 | A |
5241668 | Eastridge et al. | Aug 1993 | A |
5241670 | Eastridge et al. | Aug 1993 | A |
5276860 | Fortier et al. | Jan 1994 | A |
5276867 | Kenley et al. | Jan 1994 | A |
5287500 | Stoppani, Jr. | Feb 1994 | A |
5301286 | Rajani | Apr 1994 | A |
5321816 | Rogan et al. | Jun 1994 | A |
5333315 | Saether et al. | Jul 1994 | A |
5347653 | Flynn et al. | Sep 1994 | A |
5410700 | Fecteau et al. | Apr 1995 | A |
5420996 | Aoyagi | May 1995 | A |
5448724 | Hayashi et al. | Sep 1995 | A |
5454099 | Myers et al. | Sep 1995 | A |
5491810 | Allen | Feb 1996 | A |
5495607 | Pisello et al. | Feb 1996 | A |
5504873 | Martin et al. | Apr 1996 | A |
5544345 | Carpenter et al. | Aug 1996 | A |
5544347 | Yanai et al. | Aug 1996 | A |
5559957 | Balk | Sep 1996 | A |
5559991 | Kanfi | Sep 1996 | A |
5619644 | Crockett et al. | Apr 1997 | A |
5638509 | Dunphy et al. | Jun 1997 | A |
5642496 | Kanfi | Jun 1997 | A |
5664204 | Wang | Sep 1997 | A |
5673381 | Huai et al. | Sep 1997 | A |
5699361 | Ding et al. | Dec 1997 | A |
5729743 | Squibb | Mar 1998 | A |
5751997 | Kullick et al. | May 1998 | A |
5758359 | Saxon | May 1998 | A |
5761677 | Senator et al. | Jun 1998 | A |
5764972 | Crouse et al. | Jun 1998 | A |
5778395 | Whiting et al. | Jul 1998 | A |
5812398 | Nielsen | Sep 1998 | A |
5813009 | Johnson et al. | Sep 1998 | A |
5813017 | Morris | Sep 1998 | A |
5875478 | Blumenau | Feb 1999 | A |
5887134 | Ebrahim | Mar 1999 | A |
5901327 | Ofek | May 1999 | A |
5924102 | Perks | Jul 1999 | A |
5950205 | Aviani, Jr. | Sep 1999 | A |
5974563 | Beeler, Jr. | Oct 1999 | A |
6021415 | Cannon et al. | Feb 2000 | A |
6026414 | Anglin | Feb 2000 | A |
6052735 | Ulrich et al. | Apr 2000 | A |
6076148 | Kedem et al. | Jun 2000 | A |
6094416 | Ying | Jul 2000 | A |
6101585 | Brown et al. | Aug 2000 | A |
6131095 | Low et al. | Oct 2000 | A |
6131190 | Sidwell | Oct 2000 | A |
6148412 | Cannon et al. | Nov 2000 | A |
6154787 | Urevig et al. | Nov 2000 | A |
6161111 | Mutalik et al. | Dec 2000 | A |
6167402 | Yeager | Dec 2000 | A |
6212512 | Barney et al. | Apr 2001 | B1 |
6260069 | Anglin | Jul 2001 | B1 |
6269431 | Dunham | Jul 2001 | B1 |
6275953 | Vahalia et al. | Aug 2001 | B1 |
6301592 | Aoyama et al. | Oct 2001 | B1 |
6324581 | Xu et al. | Nov 2001 | B1 |
6327590 | Chidlovskii et al. | Dec 2001 | B1 |
6328766 | Long | Dec 2001 | B1 |
6330570 | Crighton et al. | Dec 2001 | B1 |
6330642 | Carteau | Dec 2001 | B1 |
6343324 | Hubis et al. | Jan 2002 | B1 |
RE37601 | Eastridge et al. | Mar 2002 | E |
6356801 | Goodman et al. | Mar 2002 | B1 |
6389432 | Pothapragada et al. | May 2002 | B1 |
6397242 | Devine et al. | May 2002 | B1 |
6418478 | Ignatius et al. | Jul 2002 | B1 |
6421711 | Blumenau et al. | Jul 2002 | B1 |
6487561 | Ofek et al. | Nov 2002 | B1 |
6519679 | Devireddy et al. | Feb 2003 | B2 |
6538669 | Lagueux, Jr. et al. | Mar 2003 | B1 |
6542972 | Ignatius et al. | Apr 2003 | B2 |
6564228 | O'Connor | May 2003 | B1 |
6581076 | Ching et al. | Jun 2003 | B1 |
6658436 | Oshinsky et al. | Dec 2003 | B2 |
6658526 | Nguyen et al. | Dec 2003 | B2 |
6721767 | De Meno et al. | Apr 2004 | B2 |
6760723 | Oshinsky et al. | Jul 2004 | B2 |
6772290 | Bromley et al. | Aug 2004 | B1 |
6820214 | Cabrera et al. | Nov 2004 | B1 |
6941429 | Kamvyssells et al. | Sep 2005 | B1 |
6959327 | Vogl | Oct 2005 | B1 |
6973555 | Fujiwara | Dec 2005 | B2 |
7000238 | Nadler | Feb 2006 | B2 |
7003641 | Prahlad et al. | Feb 2006 | B2 |
7035880 | Crescenti | Apr 2006 | B1 |
7076270 | Jaggers et al. | Jul 2006 | B2 |
7079341 | Kistler et al. | Jul 2006 | B2 |
7096418 | Singhal | Aug 2006 | B1 |
7107298 | Prahlad | Sep 2006 | B2 |
7130272 | Gai et al. | Oct 2006 | B1 |
7130970 | Devassy et al. | Oct 2006 | B2 |
7143203 | Altmejd | Nov 2006 | B1 |
7162496 | Amarendran et al. | Jan 2007 | B2 |
7174433 | Kottomtharayil et al. | Feb 2007 | B2 |
7219162 | Donker et al. | May 2007 | B2 |
7225220 | Gonzalez et al. | May 2007 | B2 |
7246207 | Kottomtharayil | Jul 2007 | B2 |
7260633 | Lette | Aug 2007 | B2 |
7315923 | Retnamma et al. | Jan 2008 | B2 |
7324543 | Wassew et al. | Jan 2008 | B2 |
7334144 | Schlumberger | Feb 2008 | B1 |
7340616 | Rothman et al. | Mar 2008 | B2 |
7343356 | Prahlad | Mar 2008 | B2 |
7343453 | Prahlad | Mar 2008 | B2 |
7346751 | Prahlad | Mar 2008 | B2 |
7366846 | Boyd et al. | Apr 2008 | B2 |
7376895 | Tsao | May 2008 | B2 |
7386744 | Barr | Jun 2008 | B2 |
7389311 | Crescenti et al. | Jun 2008 | B1 |
7395282 | Crescenti | Jul 2008 | B1 |
7440982 | Lu et al. | Oct 2008 | B2 |
7448079 | Tremain | Nov 2008 | B2 |
7454569 | Kavuri et al. | Nov 2008 | B2 |
7472079 | Fellenstein | Dec 2008 | B2 |
7475282 | Tormasov et al. | Jan 2009 | B2 |
7483895 | Hysom | Jan 2009 | B2 |
7484208 | Nelson | Jan 2009 | B1 |
7490207 | Amarendran et al. | Feb 2009 | B2 |
7500053 | Kavuri et al. | Mar 2009 | B1 |
7502820 | Manders | Mar 2009 | B2 |
7516346 | Pinheiro et al. | Apr 2009 | B2 |
7516348 | Ofer | Apr 2009 | B1 |
7526798 | Chao | Apr 2009 | B2 |
7529782 | Prahlad et al. | May 2009 | B2 |
7536291 | Vijayan Retnamma et al. | May 2009 | B1 |
7543125 | Gokhale | Jun 2009 | B2 |
7546324 | Prahlad et al. | Jun 2009 | B2 |
7546475 | Mayo et al. | Jun 2009 | B2 |
7552279 | Gandler | Jun 2009 | B1 |
7587570 | Sarkar et al. | Sep 2009 | B2 |
7603386 | Amarendran et al. | Oct 2009 | B2 |
7606844 | Kottomtharayil | Oct 2009 | B2 |
7613752 | Prahlad et al. | Nov 2009 | B2 |
7617191 | Wilbrink et al. | Nov 2009 | B2 |
7617253 | Prahlad et al. | Nov 2009 | B2 |
7617262 | Prahlad et al. | Nov 2009 | B2 |
7620710 | Kottomtharayil et al. | Nov 2009 | B2 |
7627827 | Taylor et al. | Dec 2009 | B2 |
7631351 | Erofeev | Dec 2009 | B2 |
7636743 | Erofeev | Dec 2009 | B2 |
7640406 | Hagerstrom et al. | Dec 2009 | B1 |
7651593 | Prahlad | Jan 2010 | B2 |
7653668 | Shelat | Jan 2010 | B1 |
7657550 | Prahlad et al. | Feb 2010 | B2 |
7660807 | Prahlad et al. | Feb 2010 | B2 |
7661028 | Erofeev | Feb 2010 | B2 |
7668884 | Prahlad | Feb 2010 | B2 |
7685177 | Hagerstrom et al. | Mar 2010 | B1 |
7685269 | Thrasher et al. | Mar 2010 | B1 |
7694070 | Mogi | Apr 2010 | B2 |
7716171 | Kryger | May 2010 | B2 |
7721138 | Lyadvinsky et al. | May 2010 | B1 |
7725671 | Prahlad et al. | May 2010 | B2 |
7725893 | Jaeckel et al. | May 2010 | B2 |
7730035 | Berger et al. | Jun 2010 | B2 |
7734669 | Kottomtharayil et al. | Jun 2010 | B2 |
7739548 | Goodrum et al. | Jun 2010 | B2 |
7747579 | Prahlad et al. | Jun 2010 | B2 |
7756835 | Pugh | Jul 2010 | B2 |
7756964 | Madison, Jr. et al. | Jul 2010 | B2 |
7761736 | Nguyen et al. | Jul 2010 | B2 |
7765167 | Prahlad | Jul 2010 | B2 |
7769616 | Ollivier | Aug 2010 | B2 |
7778984 | Zhang | Aug 2010 | B2 |
7792789 | Prahlad | Sep 2010 | B2 |
7793307 | Gokhale et al. | Sep 2010 | B2 |
7797453 | Meier et al. | Sep 2010 | B2 |
7801864 | Prahlad et al. | Sep 2010 | B2 |
7809914 | Kottomtharayil et al. | Oct 2010 | B2 |
7814149 | Stringham | Oct 2010 | B1 |
7814351 | Redlich et al. | Oct 2010 | B2 |
7818082 | Roumeliotis et al. | Oct 2010 | B2 |
7822967 | Fung | Oct 2010 | B2 |
7823145 | Le et al. | Oct 2010 | B1 |
7840537 | Gokhale | Nov 2010 | B2 |
7861234 | Lolo | Dec 2010 | B1 |
7882077 | Gokhale | Feb 2011 | B2 |
7890467 | Watanable et al. | Feb 2011 | B2 |
7899788 | Chadhok et al. | Mar 2011 | B2 |
7899791 | Gole | Mar 2011 | B1 |
7917438 | Kenedy et al. | Mar 2011 | B2 |
7917617 | Ponnapur | Mar 2011 | B1 |
7925850 | Waldspurger et al. | Apr 2011 | B1 |
7937421 | Mikesell et al. | May 2011 | B2 |
7937612 | Lyadvinsky et al. | May 2011 | B1 |
7975061 | Gokhale et al. | Jul 2011 | B1 |
7996270 | Sundaresan | Aug 2011 | B2 |
8001277 | Mega | Aug 2011 | B2 |
8037016 | Odulinski et al. | Oct 2011 | B2 |
8037028 | Prahlad | Oct 2011 | B2 |
8037032 | Pershin et al. | Oct 2011 | B2 |
8046550 | Feathergill | Oct 2011 | B2 |
8055745 | Atluri | Nov 2011 | B2 |
8060476 | Afonso et al. | Nov 2011 | B1 |
8065166 | Maresh | Nov 2011 | B2 |
8069271 | Brunet et al. | Nov 2011 | B2 |
8099391 | Monckton | Jan 2012 | B1 |
8108427 | Prahlad | Jan 2012 | B2 |
8112605 | Kavuri | Feb 2012 | B2 |
8117492 | Searls et al. | Feb 2012 | B1 |
8134727 | Shmunis | Mar 2012 | B1 |
8135930 | Mattox et al. | Mar 2012 | B1 |
8140786 | Bunte | Mar 2012 | B2 |
8156086 | Lu et al. | Apr 2012 | B2 |
8156301 | Khandelwal et al. | Apr 2012 | B1 |
8170995 | Prahlad et al. | May 2012 | B2 |
8185893 | Hyser et al. | May 2012 | B2 |
8191063 | Shingai et al. | May 2012 | B2 |
8200637 | Stringham | Jun 2012 | B1 |
8209680 | Le et al. | Jun 2012 | B1 |
8219524 | Gokhale | Jul 2012 | B2 |
8219653 | Keagy et al. | Jul 2012 | B1 |
8219769 | Wilk | Jul 2012 | B1 |
8225133 | Lyadvinsky et al. | Jul 2012 | B1 |
8229896 | Narayanan | Jul 2012 | B1 |
8229954 | Kottomtharayil et al. | Jul 2012 | B2 |
8230195 | Amarendran et al. | Jul 2012 | B2 |
8230256 | Raut | Jul 2012 | B1 |
8266406 | Kavuri | Sep 2012 | B2 |
8285681 | Prahlad | Oct 2012 | B2 |
8296534 | Gupta et al. | Oct 2012 | B1 |
8307177 | Prahlad | Nov 2012 | B2 |
8307187 | Chawla et al. | Nov 2012 | B2 |
8315992 | Gipp et al. | Nov 2012 | B1 |
8316091 | Hirvela et al. | Nov 2012 | B2 |
8321688 | Auradkar | Nov 2012 | B2 |
8352608 | Keagy et al. | Jan 2013 | B1 |
8364652 | Vijayan et al. | Jan 2013 | B2 |
8364802 | Keagy et al. | Jan 2013 | B1 |
8370307 | Wolfe | Feb 2013 | B2 |
8370542 | Lu et al. | Feb 2013 | B2 |
8396838 | Brockway et al. | Mar 2013 | B2 |
8407190 | Prahlad | Mar 2013 | B2 |
8417697 | Ghemawat et al. | Apr 2013 | B2 |
8429630 | Nickolov | Apr 2013 | B2 |
8433679 | Crescenti | Apr 2013 | B2 |
8434131 | Varadharajan et al. | Apr 2013 | B2 |
8438347 | Tawri et al. | May 2013 | B1 |
8453145 | Naik | May 2013 | B1 |
8458419 | Basler et al. | Jun 2013 | B2 |
8473594 | Astete et al. | Jun 2013 | B2 |
8489676 | Chaplin et al. | Jul 2013 | B1 |
8504515 | Prahlad et al. | Aug 2013 | B2 |
8510573 | Muller | Aug 2013 | B2 |
8527549 | Cidon | Sep 2013 | B2 |
8560788 | Sreedharan et al. | Oct 2013 | B1 |
8566362 | Mason et al. | Oct 2013 | B2 |
8577845 | Nguyen et al. | Nov 2013 | B2 |
8578120 | Attarde et al. | Nov 2013 | B2 |
8578126 | Gaonkar et al. | Nov 2013 | B1 |
8612439 | Prahlad | Dec 2013 | B2 |
8620870 | Dwarampudi et al. | Dec 2013 | B2 |
8621460 | Evans et al. | Dec 2013 | B2 |
8626741 | Vijakumar et al. | Jan 2014 | B2 |
8635184 | Hsu et al. | Jan 2014 | B2 |
8635429 | Naftel et al. | Jan 2014 | B1 |
8660038 | Pascazio | Feb 2014 | B1 |
8667171 | Guo et al. | Mar 2014 | B2 |
8674823 | Contrario et al. | Mar 2014 | B1 |
8677085 | Vaghani et al. | Mar 2014 | B2 |
8683103 | Ripberger | Mar 2014 | B2 |
8706867 | Vijayan | Apr 2014 | B2 |
8707070 | Muller | Apr 2014 | B2 |
8751857 | Frenkel et al. | Jun 2014 | B2 |
8769048 | Kottomtharayil | Jul 2014 | B2 |
8776043 | Thimsen et al. | Jul 2014 | B1 |
8780400 | Shmunis | Jul 2014 | B2 |
8799242 | Leonard et al. | Aug 2014 | B2 |
8799431 | Pabari | Aug 2014 | B2 |
8831202 | Abidogun et al. | Sep 2014 | B1 |
8849761 | Prahlad | Sep 2014 | B2 |
8849955 | Prahlad | Sep 2014 | B2 |
8850146 | Majumdar | Sep 2014 | B1 |
8904081 | Kulkarni | Dec 2014 | B1 |
8924511 | Brand | Dec 2014 | B2 |
8924967 | Nelson | Dec 2014 | B2 |
8930543 | Ashok et al. | Jan 2015 | B2 |
8938481 | Kumarasamy et al. | Jan 2015 | B2 |
8938643 | Karmarkar et al. | Jan 2015 | B1 |
8950009 | Vijayan et al. | Feb 2015 | B2 |
8954446 | Vijayan et al. | Feb 2015 | B2 |
8954796 | Cohen et al. | Feb 2015 | B1 |
8959509 | Sobel et al. | Feb 2015 | B1 |
8966318 | Shah | Feb 2015 | B1 |
9020895 | Rajashekar | Apr 2015 | B1 |
9020900 | Vijayan et al. | Apr 2015 | B2 |
9021282 | Muller et al. | Apr 2015 | B2 |
9021307 | Parameswaran et al. | Apr 2015 | B1 |
9021459 | Qu | Apr 2015 | B1 |
9026498 | Kumarasamy | May 2015 | B2 |
9069587 | Agarwal et al. | Jun 2015 | B2 |
9098457 | Towstopiat et al. | Aug 2015 | B2 |
9098495 | Gokhale | Aug 2015 | B2 |
9116633 | Sancheti et al. | Aug 2015 | B2 |
9124633 | Eizadi et al. | Sep 2015 | B1 |
9141529 | Klein et al. | Sep 2015 | B2 |
9146755 | Lassonde et al. | Sep 2015 | B2 |
9189170 | Kripalani et al. | Nov 2015 | B2 |
9195636 | Smith | Nov 2015 | B2 |
9213706 | Long et al. | Dec 2015 | B2 |
9223597 | Deshpande et al. | Dec 2015 | B2 |
9235474 | Petri et al. | Jan 2016 | B1 |
9235582 | Madiraju Varadaraju et al. | Jan 2016 | B1 |
9239687 | Vijayan et al. | Jan 2016 | B2 |
9239762 | Gunda et al. | Jan 2016 | B1 |
9246996 | Brooker | Jan 2016 | B1 |
9262496 | Kumarasamy et al. | Feb 2016 | B2 |
9268602 | Prahlad et al. | Feb 2016 | B2 |
9280378 | Shah | Mar 2016 | B2 |
9286086 | Deshpande et al. | Mar 2016 | B2 |
9286110 | Mitkar et al. | Mar 2016 | B2 |
9292350 | Pendharkar et al. | Mar 2016 | B1 |
9298715 | Kumarasamy et al. | Mar 2016 | B2 |
9307177 | Park et al. | Apr 2016 | B2 |
9311121 | Deshpande et al. | Apr 2016 | B2 |
9311248 | Wagner | Apr 2016 | B2 |
9336226 | Vibhor et al. | May 2016 | B2 |
9354927 | Hiltgen et al. | May 2016 | B2 |
9378035 | Kripalani | Jun 2016 | B2 |
9397944 | Hobbs et al. | Jul 2016 | B1 |
9405763 | Prahlad et al. | Aug 2016 | B2 |
9411534 | Lakshman et al. | Aug 2016 | B2 |
9417968 | Dornemann et al. | Aug 2016 | B2 |
9424136 | Teater et al. | Aug 2016 | B1 |
9424151 | Lakshman et al. | Aug 2016 | B2 |
9436555 | Dornemann et al. | Sep 2016 | B2 |
9444811 | Nara et al. | Sep 2016 | B2 |
9451023 | Sancheti | Sep 2016 | B2 |
9454537 | Prahlad et al. | Sep 2016 | B2 |
9461881 | Kumarasamy et al. | Oct 2016 | B2 |
9471441 | Lyadvinsky et al. | Oct 2016 | B1 |
9477683 | Ghosh | Oct 2016 | B2 |
9489244 | Mitkar et al. | Nov 2016 | B2 |
9495370 | Chatterjee et al. | Nov 2016 | B1 |
9495404 | Kumarasamy et al. | Nov 2016 | B2 |
9563514 | Dornemann | Feb 2017 | B2 |
9575789 | Rangari et al. | Feb 2017 | B1 |
9575991 | Ghosh | Feb 2017 | B2 |
9588847 | Natanzon et al. | Mar 2017 | B1 |
9594636 | Mortensen et al. | Mar 2017 | B2 |
9606745 | Satoyama et al. | Mar 2017 | B2 |
9612966 | Joshi et al. | Apr 2017 | B2 |
9632882 | Kumarasamy et al. | Apr 2017 | B2 |
9633033 | Vijayan et al. | Apr 2017 | B2 |
9639274 | Maranna et al. | May 2017 | B2 |
9639426 | Pawar et al. | May 2017 | B2 |
9639428 | Boda et al. | May 2017 | B1 |
9641388 | Kripalani et al. | May 2017 | B2 |
9648100 | Klose et al. | May 2017 | B2 |
9652283 | Mitkar et al. | May 2017 | B2 |
9665386 | Bayapuneni et al. | May 2017 | B2 |
9684535 | Deshpande et al. | Jun 2017 | B2 |
9684567 | Derk et al. | Jun 2017 | B2 |
9703584 | Kottomtharayil et al. | Jul 2017 | B2 |
9710465 | Dornemann et al. | Jul 2017 | B2 |
9766989 | Mitkar et al. | Jul 2017 | B2 |
9740702 | Pawar et al. | Aug 2017 | B2 |
9740723 | Prahlad et al. | Aug 2017 | B2 |
9760398 | Pai | Sep 2017 | B1 |
9760448 | Per | Sep 2017 | B1 |
9766825 | Bhagi et al. | Sep 2017 | B2 |
9798489 | Lakshman et al. | Oct 2017 | B2 |
9823977 | Dornemann et al. | Nov 2017 | B2 |
9852026 | Mitkar et al. | Dec 2017 | B2 |
9875063 | Lakshman | Jan 2018 | B2 |
9928001 | Dornemann et al. | Mar 2018 | B2 |
9939981 | Varadharajan et al. | Apr 2018 | B2 |
9959333 | Kumarasamy | May 2018 | B2 |
9965316 | Deshpande et al. | May 2018 | B2 |
9977687 | Kottomtharayil et al. | May 2018 | B2 |
9983936 | Dornemann et al. | May 2018 | B2 |
9996287 | Dornemann et al. | Jun 2018 | B2 |
9996534 | Dornemann et al. | Jun 2018 | B2 |
10048889 | Dornemann et al. | Aug 2018 | B2 |
10061658 | Long et al. | Aug 2018 | B2 |
10067722 | Lakshman | Sep 2018 | B2 |
10108652 | Kumarasamy et al. | Oct 2018 | B2 |
10152251 | Sancheti et al. | Dec 2018 | B2 |
10162528 | Sancheti et al. | Dec 2018 | B2 |
10162873 | Desphande et al. | Dec 2018 | B2 |
10210048 | Sancheti | Feb 2019 | B2 |
10228962 | Dornemann et al. | Mar 2019 | B2 |
10248174 | Lakshman et al. | Apr 2019 | B2 |
10248657 | Prahlad et al. | Apr 2019 | B2 |
10255143 | Vijayan et al. | Apr 2019 | B2 |
10264074 | Vijayan et al. | Apr 2019 | B2 |
10296368 | Dormemann et al. | May 2019 | B2 |
10346259 | Gokhale et al. | Jul 2019 | B2 |
10379598 | Muller | Aug 2019 | B2 |
10387073 | Bhagi et al. | Aug 2019 | B2 |
10387266 | Kumarasamy et al. | Aug 2019 | B2 |
10416919 | Cai et al. | Sep 2019 | B1 |
10417102 | Sanakkayala et al. | Sep 2019 | B2 |
10437505 | Dornemann et al. | Oct 2019 | B2 |
10452303 | Dornemann et al. | Oct 2019 | B2 |
10474483 | Kottomtharayil et al. | Nov 2019 | B2 |
10474542 | Mitkar et al. | Nov 2019 | B2 |
10474548 | Sanakkayala et al. | Nov 2019 | B2 |
10481984 | Semyonov et al. | Nov 2019 | B1 |
10496547 | Naenko | Dec 2019 | B1 |
10565067 | Dornemann | Feb 2020 | B2 |
10572468 | Dornemann et al. | Feb 2020 | B2 |
10592145 | Bedadala et al. | Mar 2020 | B2 |
10592350 | Dornemann | Mar 2020 | B2 |
10650057 | Pawar et al. | May 2020 | B2 |
10664352 | Rana | May 2020 | B2 |
10678758 | Dornemann | Jun 2020 | B2 |
10684883 | Deshpande et al. | Jun 2020 | B2 |
10684924 | Kilaru et al. | Jun 2020 | B2 |
10733143 | Pawar et al. | Aug 2020 | B2 |
10740193 | Dhatrak | Aug 2020 | B2 |
10747630 | Sanakkayala et al. | Aug 2020 | B2 |
10768971 | Dornemann et al. | Sep 2020 | B2 |
10776209 | Pawar et al. | Sep 2020 | B2 |
10776329 | Rao et al. | Sep 2020 | B2 |
10853195 | Ashraf et al. | Dec 2020 | B2 |
10872069 | Dornemann et al. | Dec 2020 | B2 |
10877928 | Nagrale et al. | Dec 2020 | B2 |
10896100 | Mitkar et al. | Jan 2021 | B2 |
10949308 | Iyer et al. | Mar 2021 | B2 |
10949398 | Mehta et al. | Mar 2021 | B2 |
10996974 | Dornemann et al. | May 2021 | B2 |
11099956 | Polimera et al. | Aug 2021 | B1 |
11223535 | Parvathamvenkatas et al. | Jan 2022 | B2 |
20020035511 | Haji | Mar 2002 | A1 |
20020069369 | Tremain | Jun 2002 | A1 |
20020083079 | Meier et al. | Jun 2002 | A1 |
20020095609 | Tokunaga | Jul 2002 | A1 |
20020129047 | Cane | Sep 2002 | A1 |
20020129106 | Gutfreund | Sep 2002 | A1 |
20020194033 | Huff | Dec 2002 | A1 |
20020194511 | Swoboda | Dec 2002 | A1 |
20030031127 | Saleh et al. | Feb 2003 | A1 |
20030126494 | Strasser | Jul 2003 | A1 |
20030140068 | Yeung | Jul 2003 | A1 |
20030200222 | Feinberg | Oct 2003 | A1 |
20030204597 | Arakawa et al. | Oct 2003 | A1 |
20040030668 | Pawlowski et al. | Feb 2004 | A1 |
20040030822 | Rajan et al. | Feb 2004 | A1 |
20040210724 | Koning et al. | Oct 2004 | A1 |
20040230899 | Pagnano et al. | Nov 2004 | A1 |
20050060356 | Saika | Mar 2005 | A1 |
20050076251 | Barr | Apr 2005 | A1 |
20050080970 | Jeyasingh et al. | Apr 2005 | A1 |
20050198303 | Knauerhase et al. | Sep 2005 | A1 |
20050216788 | Mani-Meitav et al. | Sep 2005 | A1 |
20050262097 | Sim-Tang | Nov 2005 | A1 |
20050268121 | Rothman et al. | Dec 2005 | A1 |
20050289414 | Adya | Dec 2005 | A1 |
20060058994 | Ravi | Mar 2006 | A1 |
20060064555 | Prahlad et al. | Mar 2006 | A1 |
20060101174 | Kanamaru | May 2006 | A1 |
20060101189 | Chandrasekaran et al. | May 2006 | A1 |
20060155712 | Prahlad et al. | Jul 2006 | A1 |
20060184935 | Abels et al. | Aug 2006 | A1 |
20060190775 | Aggarwal et al. | Aug 2006 | A1 |
20060195715 | Herington | Aug 2006 | A1 |
20060206507 | Dahbour | Sep 2006 | A1 |
20060212481 | Stacey et al. | Sep 2006 | A1 |
20060224846 | Amarendran | Oct 2006 | A1 |
20060225065 | Chandhok et al. | Oct 2006 | A1 |
20060230136 | Ma | Oct 2006 | A1 |
20060236073 | Soules | Oct 2006 | A1 |
20060242356 | Mogi et al. | Oct 2006 | A1 |
20060245411 | Chen et al. | Nov 2006 | A1 |
20060251067 | Desanti | Nov 2006 | A1 |
20060259908 | Bayer | Nov 2006 | A1 |
20070027999 | Allen et al. | Feb 2007 | A1 |
20070043870 | Ninose | Feb 2007 | A1 |
20070073970 | Yamazaki | Mar 2007 | A1 |
20070079156 | Fujimoto | Apr 2007 | A1 |
20070100792 | Lent et al. | May 2007 | A1 |
20070101173 | Fung | May 2007 | A1 |
20070168606 | Takai | Jul 2007 | A1 |
20070198802 | Kavuri | Aug 2007 | A1 |
20070203938 | Prahlad et al. | Aug 2007 | A1 |
20070208918 | Harbin et al. | Sep 2007 | A1 |
20070220319 | Desai et al. | Sep 2007 | A1 |
20070234302 | Suzuki | Oct 2007 | A1 |
20070239804 | Armstrong et al. | Oct 2007 | A1 |
20070266056 | Stacey et al. | Nov 2007 | A1 |
20070288536 | Sen et al. | Dec 2007 | A1 |
20080005168 | Huff et al. | Jan 2008 | A1 |
20080010521 | Goodrum et al. | Jan 2008 | A1 |
20080059704 | Kavuri | Mar 2008 | A1 |
20080071841 | Okada et al. | Mar 2008 | A1 |
20080091655 | Gokhale | Apr 2008 | A1 |
20080126833 | Callaway et al. | May 2008 | A1 |
20080134177 | Fitzgerald et al. | Jun 2008 | A1 |
20080147460 | Ollivier | Jun 2008 | A1 |
20080162592 | Huang | Jul 2008 | A1 |
20080183891 | Ni | Jul 2008 | A1 |
20080189468 | Schmidt et al. | Aug 2008 | A1 |
20080195639 | Freeman et al. | Aug 2008 | A1 |
20080228771 | Prahlad | Sep 2008 | A1 |
20080228833 | Kano | Sep 2008 | A1 |
20080229037 | Bunte | Sep 2008 | A1 |
20080235479 | Scales et al. | Sep 2008 | A1 |
20080243855 | Prahlad | Oct 2008 | A1 |
20080243947 | Kaneda | Oct 2008 | A1 |
20080244028 | Le et al. | Oct 2008 | A1 |
20080244032 | Gilson | Oct 2008 | A1 |
20080244068 | Iyoda et al. | Oct 2008 | A1 |
20080244177 | Crescenti | Oct 2008 | A1 |
20080250407 | Dadhia et al. | Oct 2008 | A1 |
20080256384 | Branson et al. | Oct 2008 | A1 |
20080270461 | Gordon | Oct 2008 | A1 |
20080270564 | Rangegowda et al. | Oct 2008 | A1 |
20080275924 | Fries | Nov 2008 | A1 |
20080282253 | Huizenga | Nov 2008 | A1 |
20080301479 | Wood | Dec 2008 | A1 |
20080313371 | Kedem et al. | Dec 2008 | A1 |
20080320319 | Muller | Dec 2008 | A1 |
20090006733 | Gold et al. | Jan 2009 | A1 |
20090037680 | Colbert et al. | Feb 2009 | A1 |
20090077443 | Nguyen et al. | Mar 2009 | A1 |
20090113109 | Nelson et al. | Apr 2009 | A1 |
20090144416 | Chatley et al. | Jun 2009 | A1 |
20090157882 | Kashyap | Jun 2009 | A1 |
20090198677 | Sheehy | Aug 2009 | A1 |
20090198825 | Miller | Aug 2009 | A1 |
20090210427 | Eidler et al. | Aug 2009 | A1 |
20090210458 | Glover et al. | Aug 2009 | A1 |
20090210464 | Chiang-Lin | Aug 2009 | A1 |
20090216816 | Basler et al. | Aug 2009 | A1 |
20090222496 | Liu et al. | Sep 2009 | A1 |
20090228669 | Siesarev et al. | Sep 2009 | A1 |
20090240904 | Austruy et al. | Sep 2009 | A1 |
20090248762 | Prahlad et al. | Oct 2009 | A1 |
20090249005 | Bender et al. | Oct 2009 | A1 |
20090268903 | Bojinov et al. | Oct 2009 | A1 |
20090282020 | McSheffrey | Nov 2009 | A1 |
20090282404 | Khandekar et al. | Nov 2009 | A1 |
20090287665 | Prahlad | Nov 2009 | A1 |
20090300023 | Vaghani | Dec 2009 | A1 |
20090300057 | Friedman | Dec 2009 | A1 |
20090307166 | Routray et al. | Dec 2009 | A1 |
20090313260 | Mimatsu | Dec 2009 | A1 |
20090313447 | Nguyen et al. | Dec 2009 | A1 |
20090313503 | Atluri et al. | Dec 2009 | A1 |
20090319534 | Gokhale | Dec 2009 | A1 |
20090319585 | Gokhale | Dec 2009 | A1 |
20090320029 | Kottomtharayil | Dec 2009 | A1 |
20090320137 | White et al. | Dec 2009 | A1 |
20090327477 | Madison, Jr | Dec 2009 | A1 |
20100011178 | Feathergill | Jan 2010 | A1 |
20100017647 | Callaway et al. | Jan 2010 | A1 |
20100023722 | Tabbara | Jan 2010 | A1 |
20100030984 | Erickson | Feb 2010 | A1 |
20100049929 | Nagarkar et al. | Feb 2010 | A1 |
20100049930 | Pershin | Feb 2010 | A1 |
20100064033 | Travostino | Mar 2010 | A1 |
20100070448 | Omoigui | Mar 2010 | A1 |
20100070466 | Prahlad | Mar 2010 | A1 |
20100070474 | Lad | Mar 2010 | A1 |
20100070725 | Prahlad | Mar 2010 | A1 |
20100070726 | Ngo et al. | Mar 2010 | A1 |
20100082672 | Kottomtharayil | Apr 2010 | A1 |
20100082700 | Parab | Apr 2010 | A1 |
20100082713 | Frid-Nielsen et al. | Apr 2010 | A1 |
20100094948 | Ganesh et al. | Apr 2010 | A1 |
20100106691 | Preslan et al. | Apr 2010 | A1 |
20100107158 | Chen et al. | Apr 2010 | A1 |
20100107172 | Calinescu et al. | Apr 2010 | A1 |
20100161919 | Dodgson et al. | Jun 2010 | A1 |
20100162002 | Dodgson et al. | Jun 2010 | A1 |
20100186014 | Vaghani et al. | Jul 2010 | A1 |
20100190478 | Brewer | Jul 2010 | A1 |
20100211829 | Ziskind et al. | Aug 2010 | A1 |
20100228913 | Czezatke et al. | Sep 2010 | A1 |
20100235333 | Bates | Sep 2010 | A1 |
20100242096 | Varadharajan et al. | Sep 2010 | A1 |
20100257403 | Virk et al. | Oct 2010 | A1 |
20100257523 | Frank | Oct 2010 | A1 |
20100262586 | Rosikiewicz et al. | Oct 2010 | A1 |
20100262794 | De Beer et al. | Oct 2010 | A1 |
20100269164 | Sosnosky et al. | Oct 2010 | A1 |
20100274772 | Samuels | Oct 2010 | A1 |
20100280999 | Atluri et al. | Nov 2010 | A1 |
20100299309 | Maki et al. | Nov 2010 | A1 |
20100299666 | Agbaria et al. | Nov 2010 | A1 |
20100306173 | Frank | Dec 2010 | A1 |
20100306486 | Balasubramanian et al. | Dec 2010 | A1 |
20100318782 | Auradkar et al. | Dec 2010 | A1 |
20100325471 | Mishra et al. | Dec 2010 | A1 |
20100325727 | Neystad et al. | Dec 2010 | A1 |
20100332401 | Prahlad | Dec 2010 | A1 |
20100332454 | Prahlad et al. | Dec 2010 | A1 |
20100332456 | Prahlad et al. | Dec 2010 | A1 |
20100332479 | Prahlad | Dec 2010 | A1 |
20100332629 | Cotugno et al. | Dec 2010 | A1 |
20100332818 | Prahlad | Dec 2010 | A1 |
20100333100 | Miyazaki et al. | Dec 2010 | A1 |
20100333116 | Prahlad | Dec 2010 | A1 |
20110004586 | Cherryholmes et al. | Jan 2011 | A1 |
20110010515 | Ranade | Jan 2011 | A1 |
20110010518 | Kavuri et al. | Jan 2011 | A1 |
20110016467 | Kane | Jan 2011 | A1 |
20110022642 | DeMilo et al. | Jan 2011 | A1 |
20110022811 | Kirihata et al. | Jan 2011 | A1 |
20110023114 | Diab et al. | Jan 2011 | A1 |
20110035620 | Elyashev et al. | Feb 2011 | A1 |
20110040824 | Harm | Feb 2011 | A1 |
20110047541 | Yamaguchi et al. | Feb 2011 | A1 |
20110055161 | Wolfe | Mar 2011 | A1 |
20110061045 | Phillips | Mar 2011 | A1 |
20110072430 | Mani | Mar 2011 | A1 |
20110087632 | Subramanian et al. | Apr 2011 | A1 |
20110107025 | Urkude et al. | May 2011 | A1 |
20110107331 | Evans et al. | May 2011 | A1 |
20110161299 | Prahlad | Jun 2011 | A1 |
20110179414 | Goggin et al. | Jul 2011 | A1 |
20110185355 | Chawla et al. | Jul 2011 | A1 |
20110191544 | Naga et al. | Aug 2011 | A1 |
20110191559 | Li et al. | Aug 2011 | A1 |
20110202728 | Nichols et al. | Aug 2011 | A1 |
20110202734 | Dhakras et al. | Aug 2011 | A1 |
20110208928 | Chandra et al. | Aug 2011 | A1 |
20110213754 | Bindal | Sep 2011 | A1 |
20110219144 | Amit et al. | Sep 2011 | A1 |
20110225277 | Freimuth et al. | Sep 2011 | A1 |
20110239013 | Muller | Sep 2011 | A1 |
20110246430 | Prahlad et al. | Oct 2011 | A1 |
20110252208 | Ali et al. | Oct 2011 | A1 |
20110264786 | Kedem et al. | Oct 2011 | A1 |
20110276713 | Brand | Nov 2011 | A1 |
20110277027 | Hayton et al. | Nov 2011 | A1 |
20120016840 | Lin et al. | Jan 2012 | A1 |
20120017027 | Baskakov et al. | Jan 2012 | A1 |
20120017043 | Aizman et al. | Jan 2012 | A1 |
20120017114 | Timashev et al. | Jan 2012 | A1 |
20120023233 | Okamoto et al. | Jan 2012 | A1 |
20120054626 | Odenheimer | Mar 2012 | A1 |
20120054736 | Arcese et al. | Mar 2012 | A1 |
20120072685 | Otani | Mar 2012 | A1 |
20120079221 | Sivasubramanian et al. | Mar 2012 | A1 |
20120084262 | Dwarampudi et al. | Apr 2012 | A1 |
20120084769 | Adi et al. | Apr 2012 | A1 |
20120096149 | Sunkara et al. | Apr 2012 | A1 |
20120110186 | Kapur et al. | May 2012 | A1 |
20120110328 | Pate et al. | May 2012 | A1 |
20120131295 | Nakajima | May 2012 | A1 |
20120131578 | Ciano et al. | May 2012 | A1 |
20120131645 | Harm | May 2012 | A1 |
20120136832 | Sadhwani | May 2012 | A1 |
20120150815 | Parfumi | Jun 2012 | A1 |
20120150818 | Vijayan Retnamma et al. | Jun 2012 | A1 |
20120150826 | Vijayan Retnamma et al. | Jun 2012 | A1 |
20120151084 | Stathopoulos et al. | Jun 2012 | A1 |
20120159232 | Zucchi | Jun 2012 | A1 |
20120167083 | Suit | Jun 2012 | A1 |
20120209812 | Bezbaruah | Aug 2012 | A1 |
20120221843 | Bak et al. | Aug 2012 | A1 |
20120233285 | Suzuki | Sep 2012 | A1 |
20120240183 | Sinha | Sep 2012 | A1 |
20120254119 | Kumarasamy et al. | Oct 2012 | A1 |
20120254364 | Vijayan | Oct 2012 | A1 |
20120254824 | Bansold | Oct 2012 | A1 |
20120278287 | Wilk | Nov 2012 | A1 |
20120278571 | Fleming et al. | Nov 2012 | A1 |
20120278799 | Starks et al. | Nov 2012 | A1 |
20120290802 | Wade et al. | Nov 2012 | A1 |
20120324183 | Chiruvolu et al. | Dec 2012 | A1 |
20120331248 | Kono et al. | Dec 2012 | A1 |
20130007245 | Malik et al. | Jan 2013 | A1 |
20130024641 | Talagala et al. | Jan 2013 | A1 |
20130024722 | Kotagiri | Jan 2013 | A1 |
20130035795 | Pfeiffer et al. | Feb 2013 | A1 |
20130036418 | Yadappanavar et al. | Feb 2013 | A1 |
20130042234 | Deluca et al. | Feb 2013 | A1 |
20130054533 | Hao et al. | Feb 2013 | A1 |
20130061014 | Prahlad et al. | Mar 2013 | A1 |
20130074181 | Singh | Mar 2013 | A1 |
20130080841 | Reddy et al. | Mar 2013 | A1 |
20130086580 | Simonsen et al. | Apr 2013 | A1 |
20130117744 | Klein et al. | May 2013 | A1 |
20130125198 | Ferguson et al. | May 2013 | A1 |
20130173771 | Ditto et al. | Jul 2013 | A1 |
20130204849 | Chacko | Aug 2013 | A1 |
20130227558 | Du et al. | Aug 2013 | A1 |
20130232215 | Gupta et al. | Sep 2013 | A1 |
20130232480 | Winterfeldt et al. | Sep 2013 | A1 |
20130238562 | Kumarasamy | Sep 2013 | A1 |
20130238572 | Prahlad et al. | Sep 2013 | A1 |
20130238969 | Smith | Sep 2013 | A1 |
20130262385 | Vibhor et al. | Oct 2013 | A1 |
20130262390 | Kumarasamy et al. | Oct 2013 | A1 |
20130262638 | Kumarasamy et al. | Oct 2013 | A1 |
20130262801 | Sancheti et al. | Oct 2013 | A1 |
20130268931 | O'Hare et al. | Oct 2013 | A1 |
20130290267 | Dwarampudi et al. | Oct 2013 | A1 |
20130297902 | Collins et al. | Nov 2013 | A1 |
20130311429 | Agetsuma | Nov 2013 | A1 |
20130326260 | Wei et al. | Dec 2013 | A1 |
20130326279 | Chavda et al. | Dec 2013 | A1 |
20140006858 | Helfman et al. | Jan 2014 | A1 |
20140007097 | Chin et al. | Jan 2014 | A1 |
20140007181 | Sarin et al. | Jan 2014 | A1 |
20140052892 | Klein et al. | Feb 2014 | A1 |
20140059306 | Bender et al. | Feb 2014 | A1 |
20140059380 | Krishnan | Feb 2014 | A1 |
20140075440 | Prahlad et al. | Mar 2014 | A1 |
20140089266 | Une et al. | Mar 2014 | A1 |
20140095816 | Hsu et al. | Apr 2014 | A1 |
20140115285 | Arcese et al. | Apr 2014 | A1 |
20140136803 | Qin | May 2014 | A1 |
20140156684 | Zaslavsky et al. | Jun 2014 | A1 |
20140181038 | Pawar et al. | Jun 2014 | A1 |
20140181044 | Pawar et al. | Jun 2014 | A1 |
20140181046 | Pawar et al. | Jun 2014 | A1 |
20140188803 | James et al. | Jul 2014 | A1 |
20140189432 | Gokhale et al. | Jul 2014 | A1 |
20140196038 | Kottomtharayil et al. | Jul 2014 | A1 |
20140196039 | Kottomtharayil et al. | Jul 2014 | A1 |
20140201150 | Kumarasamy et al. | Jul 2014 | A1 |
20140201151 | Kumarasamy et al. | Jul 2014 | A1 |
20140201157 | Pawar et al. | Jul 2014 | A1 |
20140201162 | Kumarasamy et al. | Jul 2014 | A1 |
20140201170 | Vijayan et al. | Jul 2014 | A1 |
20140237537 | Manmohan et al. | Aug 2014 | A1 |
20140244610 | Raman et al. | Aug 2014 | A1 |
20140259015 | Chigusa et al. | Sep 2014 | A1 |
20140278530 | Bruce et al. | Sep 2014 | A1 |
20140282514 | Carson et al. | Sep 2014 | A1 |
20140283010 | Rutkowski et al. | Sep 2014 | A1 |
20140330874 | Novak et al. | Nov 2014 | A1 |
20140337295 | Haselton et al. | Nov 2014 | A1 |
20140344323 | Pelavin et al. | Nov 2014 | A1 |
20140372384 | Long et al. | Dec 2014 | A1 |
20140380014 | Moyer | Dec 2014 | A1 |
20150058382 | St. Laurent | Feb 2015 | A1 |
20150067393 | Madani et al. | Mar 2015 | A1 |
20150074536 | Varadharajan et al. | Mar 2015 | A1 |
20150113055 | Vijayan et al. | Apr 2015 | A1 |
20150120928 | Gummaraju et al. | Apr 2015 | A1 |
20150121122 | Towstopiat et al. | Apr 2015 | A1 |
20150127967 | Dutton | May 2015 | A1 |
20150134607 | Magdon-Ismail et al. | May 2015 | A1 |
20150142745 | Tekade et al. | May 2015 | A1 |
20150160884 | Scales et al. | Jun 2015 | A1 |
20150161015 | Kumarasamy et al. | Jun 2015 | A1 |
20150163172 | Mudigonda et al. | Jun 2015 | A1 |
20150198995 | Muller et al. | Jul 2015 | A1 |
20150212895 | Pawar et al. | Jul 2015 | A1 |
20150227438 | Jaquette | Aug 2015 | A1 |
20150227602 | Ramu | Aug 2015 | A1 |
20150242283 | Simoncelli et al. | Aug 2015 | A1 |
20150248333 | Aravot | Sep 2015 | A1 |
20150293817 | Subramanian et al. | Oct 2015 | A1 |
20150317216 | Hsu et al. | Nov 2015 | A1 |
20150347165 | Lipchuk et al. | Dec 2015 | A1 |
20150347430 | Ghosh | Dec 2015 | A1 |
20150363413 | Ghosh | Dec 2015 | A1 |
20150370652 | He et al. | Dec 2015 | A1 |
20150378758 | Duggan et al. | Dec 2015 | A1 |
20150378771 | Rasuk-Levin | Dec 2015 | A1 |
20150378833 | Misra et al. | Dec 2015 | A1 |
20150378849 | Liu et al. | Dec 2015 | A1 |
20150381711 | Singh et al. | Dec 2015 | A1 |
20160006829 | Ishii et al. | Jan 2016 | A1 |
20160019317 | Pawar et al. | Jan 2016 | A1 |
20160042090 | Mitkar et al. | Feb 2016 | A1 |
20160070623 | Derk | Mar 2016 | A1 |
20160092467 | Lee et al. | Mar 2016 | A1 |
20160100013 | Vijayan et al. | Apr 2016 | A1 |
20160132400 | Pawar et al. | May 2016 | A1 |
20160154709 | Mitkar | Jun 2016 | A1 |
20160170844 | Long et al. | Jun 2016 | A1 |
20160188413 | Abali et al. | Jun 2016 | A1 |
20160202916 | Cui et al. | Jul 2016 | A1 |
20160210202 | Sinha | Jul 2016 | A1 |
20160283335 | Yao et al. | Sep 2016 | A1 |
20160306706 | Pawar et al. | Oct 2016 | A1 |
20160308722 | Kumarasamy et al. | Oct 2016 | A1 |
20160335007 | Ryu et al. | Nov 2016 | A1 |
20160350391 | Vijayan et al. | Dec 2016 | A1 |
20170039218 | Prahlad | Feb 2017 | A1 |
20170090972 | Ryu et al. | Mar 2017 | A1 |
20170123939 | Maheshwari et al. | May 2017 | A1 |
20170126807 | Vijayan et al. | May 2017 | A1 |
20170168903 | Dornemann et al. | Jun 2017 | A1 |
20170185488 | Kumarasamy et al. | Jun 2017 | A1 |
20170192866 | Vijayan et al. | Jul 2017 | A1 |
20170193003 | Vijayan et al. | Jul 2017 | A1 |
20170235647 | Kilaru et al. | Aug 2017 | A1 |
20170242871 | Kilaru et al. | Aug 2017 | A1 |
20170249220 | Kumarasamy et al. | Aug 2017 | A1 |
20170262204 | Dornemann et al. | Sep 2017 | A1 |
20170262347 | Dornemann | Sep 2017 | A1 |
20170262350 | Dornemann | Sep 2017 | A1 |
20170264589 | Hunt et al. | Sep 2017 | A1 |
20170286230 | Zamir | Oct 2017 | A1 |
20170286234 | Shulga | Oct 2017 | A1 |
20170371547 | Fruchtman et al. | Dec 2017 | A1 |
20170371749 | Devitt-Carolan et al. | Dec 2017 | A1 |
20180067955 | Pawar et al. | Mar 2018 | A1 |
20180075166 | Pawar et al. | Mar 2018 | A1 |
20180089031 | Dornemann et al. | Mar 2018 | A1 |
20180095845 | Sanakkayala et al. | Apr 2018 | A1 |
20180095846 | Sanakkayala et al. | Apr 2018 | A1 |
20180095855 | Sanakkayala et al. | Apr 2018 | A1 |
20180113623 | Sancheti | Apr 2018 | A1 |
20180137139 | Bangalore et al. | May 2018 | A1 |
20180143879 | Dornemann | May 2018 | A1 |
20180143880 | Dornemann | May 2018 | A1 |
20180173454 | Dornemann et al. | Jun 2018 | A1 |
20180181598 | Pawar et al. | Jun 2018 | A1 |
20180253192 | Varadharajan et al. | Sep 2018 | A1 |
20180260157 | Dornemann et al. | Sep 2018 | A1 |
20180275913 | Mitkar et al. | Sep 2018 | A1 |
20180276022 | Mitkar et al. | Sep 2018 | A1 |
20180276083 | Mitkar et al. | Sep 2018 | A1 |
20180276084 | Mitkar et al. | Sep 2018 | A1 |
20180276085 | Mitkar et al. | Sep 2018 | A1 |
20180284986 | Bhagi et al. | Oct 2018 | A1 |
20180285202 | Bhagi et al. | Oct 2018 | A1 |
20180285353 | Rao et al. | Oct 2018 | A1 |
20180285383 | Nara | Oct 2018 | A1 |
20180300168 | Deshpande et al. | Oct 2018 | A1 |
20180307510 | Kottomtharayil et al. | Oct 2018 | A1 |
20180314694 | Dornemann et al. | Nov 2018 | A1 |
20180329636 | Dornemann et al. | Nov 2018 | A1 |
20180375938 | Vijayan et al. | Dec 2018 | A1 |
20190012339 | Kumarasamy et al. | Jan 2019 | A1 |
20190026187 | Gulam et al. | Jan 2019 | A1 |
20190065069 | Sancheti et al. | Feb 2019 | A1 |
20190090305 | Hunter et al. | Mar 2019 | A1 |
20190108341 | Bedhapudi et al. | Apr 2019 | A1 |
20190179805 | Prahlad et al. | Jun 2019 | A1 |
20190182325 | Vijayan et al. | Jun 2019 | A1 |
20190278662 | Nagrale et al. | Sep 2019 | A1 |
20190303246 | Gokhale et al. | Oct 2019 | A1 |
20190340088 | Sanakkayala et al. | Nov 2019 | A1 |
20190347120 | Kottomtharayil et al. | Nov 2019 | A1 |
20190369901 | Dornemann et al. | Dec 2019 | A1 |
20190384679 | Parambil et al. | Dec 2019 | A1 |
20190391742 | Bhagi et al. | Dec 2019 | A1 |
20200034248 | Nara | Jan 2020 | A1 |
20200034252 | Mitkar et al. | Jan 2020 | A1 |
20200073574 | Pradhan | Mar 2020 | A1 |
20200117641 | Mitkar et al. | Apr 2020 | A1 |
20200142612 | Dornemann et al. | May 2020 | A1 |
20200142782 | Dornemann | May 2020 | A1 |
20200142783 | Dornemann | May 2020 | A1 |
20200162551 | Vijayan et al. | May 2020 | A1 |
20200174894 | Dornemann | Jun 2020 | A1 |
20200174895 | Dornemann | Jun 2020 | A1 |
20200183728 | Deshpande et al. | Jun 2020 | A1 |
20200265024 | Pawar et al. | Aug 2020 | A1 |
20200278915 | Degaonkar et al. | Sep 2020 | A1 |
20200319694 | Mohanty et al. | Oct 2020 | A1 |
20200349027 | Bansod et al. | Nov 2020 | A1 |
20200394107 | Rao et al. | Dec 2020 | A1 |
20200401485 | Mitkar et al. | Dec 2020 | A1 |
20200409741 | Dornemann et al. | Dec 2020 | A1 |
20210026982 | Amarendran et al. | Jan 2021 | A1 |
20210037112 | Ankireddypalle et al. | Feb 2021 | A1 |
20210049079 | Kumar et al. | Feb 2021 | A1 |
20210064485 | Rana et al. | Mar 2021 | A1 |
20210075768 | Polimera et al. | Mar 2021 | A1 |
20210089215 | Ashraf et al. | Mar 2021 | A1 |
20210173744 | Agrawal et al. | Jun 2021 | A1 |
20210208981 | Karasev | Jul 2021 | A1 |
20210255771 | Kilaru et al. | Aug 2021 | A1 |
20210271564 | Mitkar et al. | Sep 2021 | A1 |
20210286639 | Kumar | Sep 2021 | A1 |
20210357246 | Kumar et al. | Nov 2021 | A1 |
20210397522 | Owen et al. | Dec 2021 | A1 |
20220019372 | Vastrad et al. | Jan 2022 | A1 |
20220066669 | Naik et al. | Mar 2022 | A1 |
Number | Date | Country |
---|---|---|
0259912 | Mar 1988 | EP |
0405926 | Jan 1991 | EP |
0467546 | Jan 1992 | EP |
0541281 | May 1993 | EP |
0774715 | May 1997 | EP |
0809184 | Nov 1997 | EP |
817040 | Jan 1998 | EP |
0899662 | Mar 1999 | EP |
0981090 | Feb 2000 | EP |
WO9513580 | May 1995 | WO |
WO9912098 | Mar 1999 | WO |
WO 2006052872 | May 2006 | WO |
Entry |
---|
Armstead et al., “Implementation of a Campwide Distributed Mass Storage Service: The Dream vs. Reality,” IEEE, Sep. 11-14, 1995, pp. 190-199. |
Arneson, “Mass Storage Archiving in Network Environments,” Digest of Papers, Ninth IEEE Symposium on Mass Storage Systems, Oct. 31, 1988-Nov. 3, 1988, pp. 45-50, Monterey, CA. |
Bates, S. et al., “Sharepoint 2007 User's Guide,” pp. 1-88, 2007, Springer-Verlag New York, Inc., 104 pages. |
Brandon, J., “Virtualization Shakes Up Backup Strategy,” <http://www.computerworld.com>, internet accessed on Mar. 6, 2008, 3 pages. |
Cabrera et al., “ADSM: A Multi-Platform, Scalable, Backup and Archive Mass Storage System,” Digest of Papers, Compcon '95, Proceedings of the 40th IEEE Computer Society International Conference, Mar. 5, 1995-Mar. 9, 1995, pp. 420-427, San Francisco, CA. |
Chiappetta, Marco, “ESA Enthusiast System Architecture,” <http://hothardware.com/Articles/NVIDIA_ESA_Enthusiast_System_Architecture/>, Nov. 5, 2007, 2 pages. |
CommVault Systems, Inc., “A CommVault White Paper: VMware Consolidated Backup (VCB) Certification Information Kit,” 2007, 23 pages. |
CommVault Systems, Inc., “CommVault Solutions—VMware,” <http://www.commvault.com/solutions/vmware/>, internet accessed Mar. 24, 2008, 2 pages. |
CommVault Systems, Inc., “Enhanced Protection and Manageability of Virtual Servers,” Partner Solution Brief, 2008, 6 pages. |
Davis, D., “3 VMware Consolidated Backup (VCB) Utilities You Should Know,” Petri IT Knowlegebase, <http://www.petri.co.il/vmware-consolidated-backup-utilities.htm>, internet accessed on Jul. 14, 2008, Jan. 7, 2008. |
Davis, D., “Understanding VMware VMX Configuration Files,” Petri IT Knowledgebase, <http://www.petri.co.il/virtual_vmware_vmx_configuration_files.htm>, internet accessed on Jun. 19, 2008, 6 pages. |
Davis, D., “VMware Server & Workstation Disk Files Explained,” Petri IT Knowledgebase, <http://www.petri.co.il/virtual_vmware_files_explained.htm>, internet accessed on Jun. 19, 2008, 5 pages. |
Davis, D., “VMware Versions Compared,” Petri IT Knowledgebase, <http://www.petri.co.il/virtual_vmware_versions_compared.htm>, internet accessed on Apr. 28, 2008, 6 pages. |
Eitel, “Backup and Storage Management in Distributed Heterogeneous Environments,” IEEE, Jun. 12-16, 1994, pp. 124-126. |
Gait, J., “The Optical File Cabinet: A Random-Access File System For Write-Once Optical Disks,” IEEE Computer, vol. 21, No. 6, pp. 11-22 (Jun. 1988). |
International Search Report and Written Opinion for PCT/US2011/054374, dated May 2, 2012, 9 pages. |
Jander, M., “Launching Storage-Area Net,” Data Communications, US, McGraw Hill, NY, vol. 27, No. 4 (Mar. 21, 1998), pp. 64-72. |
Microsoft Corporation, “How NTFS Works,” Windows Server TechCenter, updated Mar. 28, 2003, internet accessed Mar. 26, 2008, 26 pages. |
Rosenblum et al., “The Design and Implementation of a Log-Structured File System,” Operating Systems Review SIGOPS, vol. 25, No. 5, New York, US, pp. 1-15 (May 1991). |
Sanbarrow.com, “Disktype-table,” <http://sanbarrow.com/vmdk/disktypes.html>, internet accessed on Jul. 22, 2008, 4 pages. |
Sanbarrow.com, “Files Used by a VM,” <http://sanbarrow.com/vmx/vmx-files-used-by-a-vm.html>, internet accessed on Jul. 22, 2008, 2 pages. |
Sanbarrow.com, “Monolithic Versus Split Disks,” <http://sanbarrow.com/vmdk/monolithicversusspllit.html>, internet accessed on Jul. 14, 2008, 2 pages. |
VMware, Inc., “Open Virtual Machine Format,” <http://www.vmware.com/appliances/learn/ovf.html>, internet accessed on May 6, 2008, 2 pages. |
VMware, Inc., “OVF, Open Virtual Machine Format Specification, version 0.9,” White Paper, <http://www.vmware.com>, 2007, 50 pages. |
VMware, Inc., “The Open Virtual Machine Format Whitepaper for OVF Specification, version 0.9,” White Paper, <http://www.vmware.com>, 2007, 16 pages. |
VMware, Inc., “Understanding VMware Consolidated Backup,” White Paper, <http://www.vmware.com>, 2007, 11 pages. |
Vmware, Inc., “Using VMware Infrastructure for Backup and Restore,” Best Practices, <http://www.vmware.com>, 2006, 20 pages. |
Vmware, Inc., “Virtual Disk API Programming Guide,” <http://www.vmware.com>, Revision Apr. 11, 2008, 2008, 44 pages. |
VMware, Inc., “Virtual Disk Format 1.1,” VMware Technical Note, <http://www.vmware.com>, Revision Nov. 13, 2007, Version 1.1, 2007, 18 pages. |
VMware, Inc., “Virtual Machine Backup Guide, ESX Server 3.0.1 and VirtualCenter 2.0.1,” <http://www.vmware.com>, updated Nov. 21, 2007, 74 pages. |
VMware, Inc., “Virtual Machine Backup Guide, ESX Server 3.5, ESX Server 3i version 3.5, VirtualCenter 2.5,” <http://www.vmware.com>, updated Feb. 21, 2008, 78 pages. |
VMware, Inc., “Virtualized iSCSI SANS: Flexible, Scalable Enterprise Storage for Virtual Infrastructures,” White Paper, <http://www.vmware.com>, Mar. 2008, 13 pages. |
VMware, Inc., “VMware Consolidated Backup, Improvements in Version 3.5,” Information Guide, <http://www.vmware.com>, 2007, 11 pages. |
VMware, Inc., “VMware Consolidated Backup,” Product Datasheet, <http://www.vmware.com>, 2007, 2 pages. |
VMware, Inc., “VMware ESX 3.5,” Product Datasheet, <http://www.vmware.com>, 2008, 4 pages. |
VMware, Inc., “VMware GSX Server 3.2, Disk Types: Virtual and Physical,” <http://www.vmware.com/support/gsx3/doc/disks_types_gsx.html>, internet accessed on Mar. 25, 2008, 2 pages. |
VMware, Inc., “VMware OVF Tool,” Technical Note, <http://www.vmware.com>, 2007, 4 pages. |
VMware, Inc., “VMware Workstation 5.0, Snapshots in a Linear Process,” <http:/www.vmware.com/support/ws5/doc/ws_preserve_sshot_linear.html>, internet accessed on Mar. 25, 2008, 1 page. |
VMware, Inc., “VMware Workstation 5.0, Snapshots in a Process Tree,” <http://www.vmware.com/support/ws5/doc/ws_preserve_sshot_tree.html>, internet accessed on Mar. 25, 2008, 1 page. |
VMware, Inc., “VMware Workstation 5.5, What Files Make Up a Virtual Machine?” <http://www.vmware.com/support/ws55/doc/ws_learning_files_in_a_vm.html>, internet accessed on Mar. 25, 2008, 2 pages. |
Wikipedia, “Cloud computing,” <http://en.wikipedia.org/wiki/Cloud_computing>, internet accessed Jul. 8, 2009, 13 pages. |
Wikipedia, “Cluster (file system),” <http://en.wikipedia.org/wiki/Cluster_%28file_system%29>, internet accessed Jul. 25, 2008, 1 page. |
Wikipedia, “Cylinder-head-sector,” <http://en.wikipedia.org/wiki/Cylinder-head-sector>, internet accessed Jul. 22, 2008, 6 pages. |
Wikipedia, “File Allocation Table,” <http://en.wikipedia.org/wiki/File_Allocation_Table>, internet accessed on Jul. 25, 2008, 19 pages. |
Wikipedia, “Logical Disk Manager,” <http://en.wikipedia.org/wiki/Logical_Disk_Manager>, internet accessed Mar. 26, 2008, 3 pages. |
Wikipedia, “Logical Volume Management,” <http://en.wikipedia.org/wiki/Logical_volume_management>, internet accessed on Mar. 26, 2008, 5 pages. |
Wikipedia, “Storage Area Network,” <http://en.wikipedia.org/wiki/Storage_area_network>, internet accessed on Oct. 24, 2008, 5 pages. |
Wikipedia, “Virtualization,” <http://en.wikipedia.org/wiki/Virtualization>, internet accessed Mar. 18, 2008, 7 pages. |
PTAB-IPR2021-00673—Patent Owner Mandatory Notices, filed Apr. 7, 2021, 6 pages. |
PTAB-IPR2021-00609—(′048) Popr Final, filed Jun. 16, 2021, in 28 pages. |
PTAB-IPR2021-00609—Mar. 10, 2021—IPR Petition—pty, Mar. 10, 2021, in 89 pages. |
PTAB-IPR2021-00609—Exhibit 1001—U.S. Pat. No. 10,210,048, Issue Date Feb. 19, 2019, in 49 pages. |
PTAB-IPR2021-00609—Exhibit 1002—Sandeep Expert Declaration, dated Mar. 10, 2021, in 176 pages. |
PTAB-IPR2021-00609—Exhibit 1003—U.S. Pat. No. 9,354,927 (Hiltgen), Issue Date May 31, 2016, in 35 pages. |
PTAB-IPR2021-00609—Exhibit 1004—U.S. Pat. No. 8,677,085 (Vaghani), Issue Date Mar. 18, 2014, in 44 pages. |
PTAB-IPR2021-00609—Exhibit 1005—U.S. Pat. No. 9,639,428 (Boda), Issue Date May 2, 2017, in 12 pages. |
PTAB-IPR2021-00609—Exhibit 1006—US20150212895A1 (Pawar), Publication Date Jul. 30, 2015, in 60 pages. |
PTAB-IPR2021-00609—Exhibit 1007—U.S. Pat. No. 9,665,386 (Bayapuneni), Issue Date May 30, 2017, in 18 pages. |
PTAB-IPR2021-00609—Exhibit 1008—Popek and Golberg, Jul. 1974, in 10 pages. |
PTAB-IPR2021-00609—Exhibit 1009—Virtualization Essentials—First Edition (2012)—Excerpted, 2012, in 106 pages. |
PTAB-IPR2021-00609—Exhibit 1010—Virtual Machine Monitors Current Technology and Future Trends, May 2005, in 9 pages. |
PTAB-IPR2021-00609—Exhibit 1011—Virtualization Overview, 2005, in 11 pages. |
PTAB-IPR2021-00609—Exhibit 1012 - Let's Get Virtual A Look at Today's Virtual Server, May 14, 2007, in 42 pages. |
PTAB-IPR2021-00609—Exhibit 1013—Virtual Volumes Jul. 22, 2016, in 2 pages. |
PTAB-IPR2021-00609—Exhibit 1014—Virtual Volumes and the SDDC—Virtual Blocks, Internet Archives on Sep. 29, 2015, in 4 pages. |
PTAB-IPR2021-00609—Exhibit 1015—NEC White Paper-VMWare vSphere Virtual Volumes (2015), Internet Archives Dec. 4, 2015 in 13 pages. |
PTAB-IPR2021-00609—Exhibit 1016—EMC Storage and Virtual Volumes. Sep. 16, 2015 in 5 pages. |
PTAB-IPR2021-00609—Exhibit 1017—U.S. Pat. No. 8,621,460 (Evans), Issue Date Dec. 31, 2013, in 39 pages. |
PTAB-IPR2021-00609—Exhibit 1018—U.S. Pat. No. 7,725,671 (Prahlad), Issue Date May 25, 2010, in 48 pages. |
PTAB-IPR2021-00609—Exhibit 1019—Assignment—Vaghani to VMWare, Feb. 8, 2012, in 8 pages. |
PTAB-IPR2021-00609—Exhibit 1020—Assignment Docket—Vaghani, Nov. 11, 2011, in 1 page. |
PTAB-IPR2021-00609—Exhibit 1021—Dive into the VMware ESX Server hypervisor-IBM Developer, Sep. 23, 2011, in 8 pages. |
PTAB-IPR2021-00609—Exhibit 1022—MS Computer Dictionary Backup labeled, 2002 in 3 pages. |
PTAB-IPR2021-00609—Exhibit 1023—Jul. 7, 2014_VMware vSphere Blog, Jun. 30, 2014, 4 pages. |
PTAB-IPR2021-00609—Exhibit 1024—CommVault v. Rubrik Complaint, filed on Apr. 21, 2020, in 29 pages. |
PTAB-IPR2021-00609—Exhibit 1025—CommVault v. Cohesity Complaint, filed on Apr. 21, 2020, in 28 pages. |
PTAB-IPR2021-00609—Exhibit 1026—Feb. 17, 2021 (0046) Scheduling Order, filed on Feb. 17, 2021, in 15 pages. |
PTAB-IPR2021-00609—Exhibit 2001—Prosecution History_Part1, Issue Date Feb. 19, 2019, in 300 pages, Part 1 of 2. |
PTAB-IPR2021-00609—Exhibit 2001—Prosecution History_Part2, Issue Date Feb. 19, 2019, in 265 pages, Part 2 of 2. |
PTAB-IPR2021-00609—Exhibit 2002—Jones Declaration, dated Jun. 16, 2021, in 38 pages. |
PTAB-IPR2021-00609—Exhibit 3001—RE_IPR2021-00535, 2021-00589, 2021-00590, 2021-00609, 2021-00673, 2021-00674, 2021-00675, dated Aug. 30, 2021, in 2 pages. |
PTAB-IPR2021-00609—Joint Motion to Terminate. Filed Aug. 31, 2021, in 7 pages. |
PTAB-IPR2021-00609—Joint Request to Seal Settlement Agreement, filed Aug. 31, 2021, in 4 pages. |
PTAB-IPR2021-00609—Termination Order, Sep. 1, 2021, in 4 pages. |
Case No. No. 6:21-CV-00634-ADA, Answer WDTX-6-21-cv-00634-1 9, filed Aug. 27, 2021, in 23 pages. |
Case No. 1:21-cv-00537, Complaint WDTX-1-21-cv-00537-1_WDTX-6-21-cv-00634-1, filed Jun. 18, 2021, in 44 pages. |
Case No. 6:21-cv-00634-ADA, Order Dismissing with Prejudice WDTX-6-21-cv-00634-22, filed Sep. 1, 2021, in 1 page. |
PTAB-IPR2021-00673—(′ 723) Popr Final, filed Jun. 30, 2021, in 70 pages. |
PTAB-IPR2021-00673—(′723) Sur-Reply FINAL, filed Aug. 16, 2021, in 7 pages. |
PTAB-IPR2021-00673—723 patent IPR—Reply to POPR, filed Aug. 9, 2021, in 6 pages. |
PTAB-IPR2021-00673—Mar. 17, 2021_Petition_723, filed Mar. 17, 2021, in 98 pages. |
PTAB-IPR2021-00673—Exhibit 1001—U.S. Pat. No. 9,740,723, Issue Date Aug. 22, 2017, in 51 pages. |
PTAB-IPR2021-00673—Exhibit 1002—Declaration_Jagadish_EXSRanger, filed Mar. 16, 2021, in 191 pages. |
PTAB-IPR2021-00673—Exhibit 1003—FH 9740723, Issue Date Aug. 22, 2017, in 594 pages. |
PTAB-IPR2021-00673—Exhibit 1004—esxRangerProfessionalUserManual v.3.1, 2006 in 102 pages. |
PTAB-IPR2021-00673—Exhibit 1005—VC_Users_Manual_11_NoRestriction, Copyright date 1998-2004, in 466 pages. |
PTAB-IPR2021-00673—Exhibit 1006—U.S. Pat. No. 8,635,429—Naftel, Issue Date Jan. 21, 2014, in 12 pages. |
PTAB-IPR2021-00673—Exhibit 1007—US20070288536A1—Sen, Issue Date Dec. 13, 2007, in 12 pages. |
PTAB-IPR2021-00673—Exhibit 1008—US20060224846A1—Amarendran, Oct. 5, 2006, in 15 pages. |
PTAB-IPR2021-00673—Exhibit 1009—U.S. Pat. No. 8209680—Le, Issue Date Jun. 26, 2012, in 55 pages. |
PTAB-IPR2021-00673—Exhibit 1010—Virtual Machine Monitors Current Technology and Future Trends, May 2005 in 9 pages. |
PTAB-IPR2021-00673—Exhibit 1011—Virtualization Overview, Copyright 2005, VMware, Inc., 11 pages. |
PTAB-IPR2021-00673—Exhibit 1012—Let's Get Virtual A Look at Today's Virtual Server, May 14, 2007 in 42 pages. |
PTAB-IPR2021-00673—Exhibit 1013—U.S. Pat. No. 8,135,930—Mattox, Issue Date Mar. 13, 2012, in 19 pages. |
PTAB-IPR2021-00673—Exhibit 1014—U.S. Pat. No. 8,060,476—Afonso, Issue Date Nov. 15, 2011, in 46 pages. |
PTAB-IPR2021-00673—Exhibit 1015—U.S. Pat. No. 7,823,145—Le 145, Issue Date Oct. 26, 2010, in 24 pages. |
PTAB-IPR2021-00673—Exhibit 1016—US20080091655A1—Gokhale, Publication Date Apr. 17, 2008, in 14 pages. |
PTAB-IPR2021-00673—Exhibit 1017—US20060259908A1—Bayer, Publication Date Nov. 16, 2006, in 8 pages. |
PTAB-IPR2021-00673—Exhibit 1018—U.S. Pat. No. 8,037,016—Odulinski, Issue Date Oct. 11, 2011, in 20 pages. |
PTAB-IPR2021-00673—Exhibit 1019—U.S. Pat. No. 7,9258,50—Waldspurger, Issue Date Apr. 12, 2011, in 23 pages. |
PTAB-IPR2021-00673—Exhibit 1020—U.S. Pat. No. 8,191,063—Shingai, May 29, 2012, in 18 pages. |
PTAB-IPR2021-00673—Exhibit 1021—U.S. Pat. No. 8,959,509B1—Sobel, Issue Date Feb. 17, 2015, in 9 pages. |
PTAB-IPR2021-00673—Exhibit 1022—U.S. Pat. No. 8,458,419—Basler, Issue Date Jun. 4, 2013, in 14 pages. |
PTAB-IPR2021-00673—Exhibit 1023—D. Hall_lnternet Archive Affidavit & Ex. A, dated Jan. 20, 2021, in 106 pages. |
PTAB-IPR2021-00673—Exhibit 1024—esxRangerProfessionalUserManual, 2006, in 103 pages. |
PTAB-IPR2021-00673—Exhibit 1025—D.Hall_lnternet Archive Affidavit & Ex. A (source html view), dated Jan. 27, 2021, in 94 pages. |
PTAB-IPR2021-00673—Exhibit 1026—Scripting VMware (excerpted) (GMU), 2006, in 19 pages. |
PTAB-IPR2021-00673—Exhibit 1027—How to cheat at configuring VMware ESX server (excerpted), 2007, in 16 pages. |
PTAB-IPR2021-00673—Exhibit 1028—Robs Guide to Using VMware (excerpted), Sep. 2005 in 28 pages. |
PTAB-IPR2021-00673—Exhibit 1029—Hall-Ellis Declaration, dated Feb. 15, 2021, in 55 pages. |
PTAB-IPR2021-00673—Exhibit 1030—B. Dowell declaration, dated Oct. 15, 2020, in 3 pages. |
PTAB-IPR2021-00673—Exhibit 1031—Vizioncore esxEssentials Review ZDNet, Aug. 21, 2007, in 12 pages. |
PTAB-IPR2021-00673—Exhibit 1032—ZDNet Search on_ howorth—p. 6 _, printed on Jan. 15, 2021, ZDNet 3 pages. |
PTAB-IPR2021-00673—Exhibit 1033—ZDNet _ Reviews _ ZDNet, printed on Jan. 15,02021, in 33 pages. |
PTAB-IPR2021-00673—Exhibit 1034—Understanding VMware Consolidated Backup, 2007, 11 pages. |
PTAB-IPR2021-00673—Exhibit 1035—techtarget.com news links—May 2007, May 20, 2007, in 39 pages. |
PTAB-IPR2021-00673—Exhibit 1036—ITPro 2007 Issue 5 (excerpted), Sep.-Oct. 2007 in 11 pages. |
PTAB-IPR2021-00673—Exhibit 1037—InfoWorld—Feb. 13, 2006, Feb. 13, 2006, in 17 pages. |
PTAB-IPR2021-00673—Exhibit 1038—InfoWorld—Mar. 6, 2006, Mar. 6, 2006, in 18 pages. |
PTAB-IPR2021-00673—Exhibit 1039—InfoWorld—Apr. 10, 2006, Apr. 10, 2006, in 18 pages. |
PTAB-IPR2021-00673—Exhibit 1040—InfoWorld—Apr. 17, 2006, Apr. 17, 2006, in 4 pages. |
PTAB-IPR2021-00673—Exhibit 1041—InfoWorld—May 1, 2006, May 1, 2006, in 15 pages. |
PTAB-IPR2021-00673—Exhibit 104—InfoWorld—Sep. 25, 2006, Sep. 25, 2006, in 19 pages. |
PTAB-IPR2021-00673—Exhibit 1043—InfoWorld—Feb. 5, 2007, Feb. 5, 2007, in 22 pages. |
PTAB-IPR2021-00673—Exhibit 1044—InfoWorld—Feb. 12, 2007, Feb. 12, 2007, in 20 pages. |
PTAB-IPR2021-00673—Exhibit 1045—Informationweek—Aug. 14, 2006, Aug. 14, 2006, in 17 pages. |
PTAB-IPR2021-00673—Exhibit 1046—esxRanger Ably Backs Up VMs, May 2, 2007 in 6 pages. |
PTAB-IPR2021-00673—Exhibit 1047—Businesswire—Vizioncore Inc. Releases First Enterprise-Class Hot Backup and Recovery Solution for VMware Infrastructure, Aug. 31, 2006 in 2 pages. |
PTAB-IPR2021-00673—Exhibit 1048—Vizioncore Offers Advice to Help Users Understand VCB for VMwar, Jan. 23, 2007 in 3 pages. |
PTAB-IPR2021-00673—Exhibit 1049—Dell Power Solutions—Aug. 2007 (excerpted), Aug. 2007 in 21 pages. |
PTAB-IPR2021-00673—Exhibit 1050—communities-vmware-t5-VI-VMware-ESX-3-5-Discussions, Jun. 28, 2007, in 2 pages. |
PTAB-IPR2021-00673—Exhibit 1051—Distributed_File_System_Virtualization, Jan. 2006, pp. 45-56, in 12 pages. |
PTAB-IPR2021-00673—Exhibit 1052—Distributed File System Virtualization article abstract, 2006, in 12 pages. |
PTAB-IPR2021-00673—Exhibit 1053—Cluster Computing _ vol. 9, issue 1, Jan. 2006 in 5 pages. |
PTAB-IPR2021-00673—Exhibit 1054—redp3939—Server Consolidation with VMware ESX Server, Jan. 12, 2005 in 159 pages. |
PTAB-IPR2021-00673—Exhibit 1055—Server Consolidation with VMware ESX Server _ Index Page, Jan. 12, 2005 in 2 pages. |
PTAB-IPR2021-00673—Exhibit 1056—Apr. 21, 2020 [1] Complaint, filed Apr. 21, 2020, in 300 pages. |
PTAB-IPR2021-00673—Exhibit 1057—Feb. 17, 2021 (0046) Scheduling Order, filed Feb. 17, 2021, in 15 pages. |
PTAB-IPR2021-00673—Exhibit 1058—Novell Netware 5.0-5.1 Network Administration (Doering), Copyright 2001 in 40 pages. |
PTAB-IPR2021-00673—Exhibit 1059—US20060064555A1 (Prahlad 555), Publication Date Mar. 23, 006, in 33 pages. |
PTAB-IPR2021-00673—Exhibit 1060—Carrier Book, 2005 in 94 pages. |
PTAB-IPR2021-00673—Exhibit 2001 Jones Declaration, filed Jun. 30, 2021, in 35 pages. |
PTAB-IPR2021-00673—Exhibit 2002 VM Backup Guide 3.0.1, updated Nov. 21, 2007, 74 pages. |
PTAB-IPR2021-00673—Exhibit 2003 VM Backup Guide 3.5, updated Feb. 21, 2008, 78 pages. |
PTAB-IPR2021-00673—Exhibit 3001 RE_IPR2021-00535, 2021-00589, 2021-00590, 2021-00609, 2021-00673, 2021-00674, 2021-00675, Aug. 30, 2021, in 2 pages. |
PTAB-IPR2021-00673—Joint Motion to Terminate, filed Aug. 31, 2021, in 7 pages. |
PTAB-IPR2021-00673—Joint Request to Seal Settlement Agreement, filed Aug. 31, 2021, in 4 pages. |
PTAB-IPR2021-00673—673 674 Termination Order, Sep. 1, 2021, in 4 pages. |
U.S. Appl. No. 16/792,026, filed Feb. 14, 2020, Kilaru et al. |
U.S. Appl. No. 16/816,934, filed Mar. 12, 2020, Kumar. |
U.S. Appl. No. 16/831,562, filed Mar. 26, 2020, Polimera et al. |
U.S. Appl. No. 17/153,667, filed Jan. 20, 2021, Naik et al. |
U.S. Appl. No. 17/336,103, filed Jun. 1, 2021, Vastrad et al. |
U.S. Appl. No. 63/081,503, filed Sep. 22, 2020, Jain et al. |
U.S. Appl. No. 63/082,624, filed Sep. 24, 2020, Camargos et al. |
Case No. 1:20-cv-00525-CFC-CJB, Joint Appendix of Exhibits 1-6, filed Jan. 13, 2022, in 224 pages. |
Case No. 1:20-cv-00525-CFC-CJB, Joint Claim Construction Brief On Remaining Disputed Terms, filed Jan. 13, 2022, in 54 pages. |
Case No. 20-525-CFC-CJB, Stipulation of Dismissal, filed Jan. 27, 2022, in 2 pages. |
PTAB-IPR2021-00674—(723) Popr Final, filed Jul. 8, 2021, in 70 pages. |
PTAB-IPR2021-00674—Mar. 31, 2021 723 Petition, filed Mar. 31, 2021, in 87 pages. |
PTAB-IPR2021-00674—Mar. 31, 2021 Explanation for Two Petitions, filed Mar. 31, 2021, in 9 pages. |
PTAB-IPR2021-00674—Exhibit 1001—U.S. Pat. No. 9,740,723, Issue Date Aug. 22, 2017, in 51 pages. |
PTAB-IPR2021-00674—Exhibit 1002—Jagadish Declaration, dated Mar. 31, 2021, in 200 pages. |
PTAB-IPR2021-00674—Exhibit 1003—U.S. Pat. No. 9,740,723 file history, Issue Date Aug. 22, 2017, in 594 pages. |
PTAB-IPR2021-00674—Exhibit 1004—Virtual Machine Monitors Current Technology and Future Trends, May 2005, in 9 pages. |
PTAB-IPR2021-00674—Exhibit 1005—Virtualization Overview, 2005, 11 pages. |
PTAB-IPR2021-00674—Exhibit 1006—Let's Get Virtual_Final Stamped, May 14, 2007, in 42 pages. |
PTAB-IPR2021-00674—Exhibit 1007—U.S. Pat. No. 8,458,419—Basler, Issue Date Jun. 4, 2013, in 14 pages. |
PTAB-IPR2021-00674—Exhibit 1008—US20080244028A1 (Le), Publication Date Oct. 2, 2008, in 22 pages. |
PTAB-IPR2021-00674—Exhibit 1009—60920847 (Le Provisional), Filed Mar. 29, 2007, in 70 pages. |
PTAB-IPR2021-00674—Exhibit 1010—Discovery Systems in Ubiquitous Computing (Edwards), 2006, in 8 pages. |
PTAB-IPR2021-00674—Exhibit 1011 - HTTP The Definitive Guide excerpts (Gourley), 2002, in 77 pages. |
PTAB-IPR2021-00674—Exhibit 1012—VCB White Paper (Wayback Mar. 21, 2007), retrieved Mar. 21, 2007, Coypyright Date 1998-2006, in 6 pages. |
PTAB-IPR2021-00674—Exhibit 1013—Scripting VMware excerpts (Muller), 2006, in 66 pages. |
PTAB-IPR2021-00674—Exhibit 1014—Rob's Guide to Using VMWare excerpts (Bastiaansen), Sep. 2005, in 178 pages. |
PTAB-IPR2021-00674—Exhibit 1015—Carrier, 2005 in 94 pages. |
PTAB-IPR2021-00674—Exhibit 1016—U.S. Pat. No. 7,716,171 (Kryger), Issue Date May 11, 2010, in 18 pages. |
PTAB-IPR2021-00674—Exhibit 1017—RFC2609, Jun. 1999, in 33 pages. |
PTAB-IPR2021-00674—Exhibit 1018—MS Dictionary excerpt, 2002, in 3 pages. |
PTAB-IPR2021-00674—Exhibit 1019—Commvault v. Rubrik Complaint, Filed Apr. 21, 2020, in 29 pages. |
PTAB-IPR2021-00674—Exhibit 1020—Commvault v. Rubrik Scheduling Order, Filed Feb. 17, 2021, in 15 pages. |
PTAB-IPR2021-00674—Exhibit 1021—Duncan Affidavit, Dated Mar. 3, 2021, in 16 pages. |
PTAB-IPR2021-00674—Exhibit 1022—Hall-Ellis Declaration, dated Mar. 30, 2021, in 291 pages. |
PTAB-IPR2021-00674—Exhibit 1023—Digital_Data_Integrity_2007_Appendix_A_UMCP, 2007, in 24 pages. |
PTAB-IPR2021-00674—Exhibit 1024—Rob's Guide—Amazon review (Jan. 4, 2007), retrieved Jan. 4, 2007, in 5 pages. |
PTAB-IPR2021-00674—Exhibit 2001—esxRanger, 2006, in 102 pages. |
PTAB-IPR2021-00674—Exhibit 2002—Want, 1995, in 31 pages. |
PTAB-IPR2021-00674—Exhibit 2003—Shea, retrieved Jun. 10, 2021, in 5 pages. |
PTAB-IPR2021-00674—Exhibit 2004—Jones Declaration, Dated Jul. 8, 2021, in 36 pages. |
PTAB-IPR2021-00674—Exhibit 3001, dated Aug. 30, 2021, in 2 pages. |
PTAB-IPR2021-00674—Exhibit IPR2021-00674 Joint Request to Seal Settlement Agreement, dated Aug. 31, 2021, in 4 pages. |
PTAB-IPR2021-00674—Joint Motion to Terminate, Filed Aug. 31, 2021, in 7 pages. |
PTAB-IPR2021-00674—Response to Notice Ranking Petitions FINAL, filed Jul. 8, 2021, in 7 pages. |
PTAB-IPR2021-00674—Termination Order, filed Sep. 1, 2021, in 4 pages. |
Case No. 1:20-cv-00525-MN, Amended Complaint DDE-1-20-cv-00525-15, filed Jul. 27, 2020, in 30 pages. |
Case No. 1:20-cv-00525-MN, Complaint DDE-1-20-cv-00525-1, Apr. 21, 2020, in 28 pages. |
Case No. 1:20-cv-00525-MN, First Amended Answer DDE-1-20-cv-00525-95, filed Jul. 23, 2021, in 38 pages. |
Case No. 1:20-cv-00525-MN, Joint Claim Construction Brief Exhibits DDE-1-20-cv-00525-107-1, filed Oct. 1, 2021, in 488 pages (in 7 parts). |
Case No. 1:20-cv-00525-MN, Oral Order DDE-1-20-cv-00524-78_DDE-1-20-cv-00525-77, dated May 24, 2021, in 1 page. |
Case No. 1:20-cv-00525-MN, Oral Order DDE-1-20-cv-00524-86_DDE-1-20-cv-00525-87, dated Jun. 29, 2021, in 1 page. |
Case No. 1:20-cv-00525-MN, Order DDE-1-20-cv-00525-38_DDE-1-20-cv-00524-42, filed Feb. 10, 2021, in 4 pages. |
Case No. 20-525-MN-CJB, Joint Claim Construction Brief DDE-1-20-cv-00525-107, filed Oct. 1, 2021, in 79 pages. |
Case No. 20-525-MN-CJB, Joint Claim Construction Statement DDE-1-20-cv-00525-119, filed Oct. 29, 2021, in 12 pages. |
Case No. 20-525-MN-CJB, Farnan Letter DDE-1-20-cv-00525-111, filed Oct. 6, 2021, in 2 pages. |
Case No. 20-525-MN-CJB, Farnan Letter Exhibit A DDE-1-20-cv-00525-111-1, filed Oct. 6, 2021, in 7 pages. |
Case No. 1:20-cv-00524-MN,Order Dismissing with Prejudice DDE-1-20-cv-00524-101, filed Aug. 31, 2021, in 1 page. |
Case No. 1:20-cv-00524-MN,Amended_Complaint_DDE-1-20-cv-00524-13, filed on Jul. 27, 2020 in 30 pages. |
Case No. 1:20-cv-00524-MN,First_Amended_Answer_DDE-1-20-cv-00524-96, filed Jul. 23, 2021, in 41 pages. |
Case No. 1:20-cv-00524-MN,Stipulation_DDE-1-20-cv-00524-93, filed Jul. 14, 2021, in 3 pages. |
Case No. 1:20-cv-00524-MN,Oral Order DDE-1-20-cv-00524-86_DDE-1-20-cv-00525-87, filed Jun. 29, 2021, in 1 page. |
Case No. 1:20-cv-00524-MN,Complaint_DDE-1-20-cv-00524-1, filed on Apr. 21, 2020 in 28 pages. |
Case No. 1:20-cv-00524-MN,Answer_DDE-1-20-cv-00524-45, filed Feb. 16, 2021, in 25 pages. |
Case No. 1:20-cv-00524-MN,Order_DDE-1-20-cv-00525-38_DDE-1-20-cv-00524-42, filed Feb. 10, 2021, in 4 pages. |
U.S. Appl. No. 16/262,753, filed Jan. 30, 2019, Dornemann et al.. |
U.S. Appl. No. 16/721,644, filed Dec. 19, 2019, Kumar et al.. |
U.S. Appl. No. 16/868,964, filed May 7, 2020, Mitkar et al.. |
U.S. Appl. No. 16/907,023, filed Jun. 19, 2020, Owen et al.. |
U.S. Appl. No. 16/914,020, filed Jun. 26, 2020, Amarendran et al.. |
U.S. Appl. No. 17/079,023, filed Oct. 23, 2020, Agrawal et al.. |
U.S. Appl. No. 17/114,296, filed Dec. 7, 2020, Ashraf et al.. |
U.S. Appl. No. 17/129,581, filed Dec. 21, 2020, Parvathamvenkatas et al.. |
U.S. Appl. No. 17/130,540, filed Dec. 22, 2020, Kumar et al.. |
U.S. Appl. No. 17/501,881, filed Oct. 14, 2021, Dornemann et al.. |
U.S. Appl. No. 63/082,631, filed Sep. 24, 2020, Kavaipatti Anantharamakrishnan et al.. |
Celesti et al., “Improving Virtual Machine Migration in Federated Cloud Environments”, 2010, pp. 61-67. |
Chan, et al., “An Approach to High Availability for Cloud Servers with Snapshot Mechanism,” 2012, pp. 1-6. |
Chen et al., “When Virtual Is Better Than Real”, IEEE 2001, pp. 133-138. |
Chervenak, et al., “Protecting File Systems-A Survey of Backup Techniques,” 1998, pp. 17-31. |
CommVault Systems, Inc., “CommVault Solutions—VMware,” <http://www.commvault.com/solutions/vmware/>, accessed Apr. 30, 2014, 1 page. |
Cully, et al., “Remus: High Availability via Asynchronous Virtual Machine Replication”, 2008, pp. 161-174. |
Data Protection for Large Vmware and Vblock Environments Using EMC Avamar Applied Technology, Nov. 2010, EMC Corporation, 26 pages. |
Davis, D., “3 VMware Consolidated Backup (VCB) Utilities You Should Know,” Petri IT Knowlegebase, <http://www.petri.co.il/vmware-consolidated-backup-utilities.htm>, Nov. 16, 2007, 3 pages. |
Davis, D., “Understanding VMware VMX Configuration Files,” Petri IT Knowledgebase, <http://www.petri.co.il/virtual_vmware_vmx_configuration_files.htm>, Nov. 16, 2007, 3 pages. |
Davis, D., “VMware Server & Workstation Disk Files Explained,” Petri IT Knowledgebase, <http://www.petri.co.il/virtual_vmware_files_explained.htm>, May 3, 2008, 3 pages. |
Davis, D., “VMware Versions Compared,” Petri IT Knowledgebase, <http://www.petri.co.il/virtual_vmware_versions_compared.htm>, Nov. 16, 2007, 3 pages. |
Deng, et al., “Fast Saving and Restoring Virtual Machines with p. Compression”, 2011, pp. 150-157. |
Eldos Callback File System product information from https://www.eldos.com/clients/104-345.php retrieved on Dec. 30, 2016 in 2 pages. |
Eldos Usermode filesystem for your Windows applications—Callback File System® (CBFS®)- Create and manage virtual filesystems and disks from your Windows applications retrieved from https://eldos.com/cbfs on Dec. 30, 2016 in 4 pages. |
Fraser, et al., “Safe Hardware Access With the Xen Virtual Machine Monitor”, 1st Workshop on Operating System and Architectural Support for the demand IT Infrastructure (OASIS), 2004, pp. 1-10. |
Galan et al. “Service Specification in Cloud Environments Based on Extension to Oper Standards” COMSWARE Jun. 16-19, 09 Dublin, Ireland ACM. |
Gibson, et al., “Implementing Preinstallation Environment Media for Use in User Support,” 2007, pp. 129-130. |
Granger, et al., “Survivable Storage Systems”, 2001, pp. 184-195. |
Gupta, et al., “GPFS-SNC: An enterprise storage framework for virtual-machine clouds”, 2011, pp. 1-10. |
Haselhorst, et al., “Efficient Storage Synchronization for Live Migration in Cloud Infrastructures”, 2011, pp. 511-518. |
Hirofuchio, Takahiro et al., “A live storage migration mechanism over wan and its performance evaluation,” 2009, pp. 67-74. |
Hirofuchi, et al., “Enabling Instantaneous Relocation of Virtual Machines with a Lightweight VMM Extension”, 2010, pp. 73-83. |
Hu, et al., “Virtual Machine based Hot-spare Fault-tolerant System”, 2009, pp. 429-432. |
Hu, Wenjin et al., “A Quantitative Study of Virtual Machine Live Migration,” 2013, pp. 1-10. |
Huff, “Data Set Usage Sequence Number,” IBM Technical Disclosure Bulletin, vol. 24, No. 5, Oct. 1981 New York, US, pp. 2404-2406. |
Ibrahim, Shadi et al., “CLOUDLET: Towards MapReduce Implementation on Virtual Machines,” 2009, pp. 65-66. |
Ismail et al., Architecture of Scalable Backup Service. |
Javaraiah, et al., “Backup for Cloud and Disaster Recovery for Consumers and SMBs,” 2008, pp. 1-3. |
Jhawar et al., “Fault Tolerance Management in Cloud Computing: A System-Level Perspective”, IEEE Systems Journal 7.2, 2013, pp. 288-297. |
Jo, et al., “Efficient Live Migration of Virtual Machines Using Shared Storage”, 2013, pp. 1-10. |
Kashyap “RLC—A Reliable approach to Fast and Efficient Live Migration of Virtual Machines in the Clouds” IEEE 2014 IEEE Computer Society. |
Kim, et al., “Availability Modeling and Analysis of a Virtualized System,” 2009, pp. 365-371. |
Kuo, et al., “A Hybrid Cloud Storage Architecture for Service Operational High Availability”, 2013, pp. 487-492. |
Li et al. “Comparing Containers versus Virtual Machines for Achieving High Availability” 2015 IEEE. |
Liang, et al., “A virtual disk environment for providing file system recovery”, 2006, pp. 589-599. |
Lu et al.,. “Virtual Machine Memory Access Tracing with Hypervisor Exclusive Cache”, Usenix Annual Technical Conference, 2007, pp. 29-43. |
Mao, et al., “Read-Performance Optimization for Deduplication-Based Storage Systems in the Cloud”, 2014, pp. 1-22. |
Migrate a Virtual Machine with Storage vMotion in the vSphere Client. http://pubs.vmware.com/vsphere-51/advanced/print/jsp?topic=/com.vmware.vsphere.vcent . . . Retrieved Aug. 12, 2014; 2 pages. |
Nance et al., “Virtual Machine Introspection: Observation or Interference?”, 2008 IEEE. |
Ng, Chun-Ho et al., “Live Deduplication Storage of Virtual Machine Images in an Open-Source Cloud,” 2011, pp. 80-99. |
Nicolae, Bogdan et al., “A Hybrid Local Storage Transfer Scheme for Live Migration of 1/0 Intensive Workloads,” 2012, pp. 85-96. |
Reingold, B. et al., “Cloud Computing: The Intersection of Massive Scalability, Data Security and Privacy (Part I),” LegalWorks, a Thomson Business, Jun. 2009, 5 pages. |
Sanbarrow.com, “Disktype-table,” <http://sanbarrow.com/vmdk/disktypes.html>, internet accessed on Apr. 30, 2014, 4 pages. |
Sanbarrow.Com, “Files Used by a VM,” <http://sanbarrow.com/vmx/vmx-files-used-by-a-vm.html>, internet accessed on Apr. 30, 2014, 1 page. |
Somasundaram et al., Information Storage and Management. 2009, pp. 251-281. |
Tran, et al., “Efficient Cooperative Backup with Decentralized Trust Management”, 2012, pp. 1-25. |
Travostino, et al., “Seamless live migration of virtual machines over the MAN/WAN”, 2006, pp. 901-907. |
Tudoran, Radu et al., “Adaptive File Management for Scientific Workflows on the Azure Cloud,” 2013, pp. 273-281. |
Vaghani, “Virtual Machine File System”, 2010, pp. 57-70. |
VMware, Inc., “VMware Solution Exchange (VSX)” <http://www.vmware.com/appliances/learn/ovf.html>, 2014, 3 pages. |
VMware, Inc., “VMware Consolidated Backup, Improvements in Version 3.5,” Information Guide, <http://www.vmware.com>, accessed Apr. 30, 2014, 11 pages. |
VMware Storage VMotion—Non-Disruptive Live Migration for Virtual Machine Storage Disk Files. Copyright 2009 VMware, Inc.; 2 pages. |
Vrable, et al., “Cumulus: Filesystem Backup to the Cloud”, 2009, pp. 1-28. |
vSphere Storage vMotion: Storage Management & Virtual Machine Migration. http://www.vmware.com/products /vsphere/features/storage-vmotion, retrieved Aug. 12, 2014; 6 pages. |
Wood, et al., “Disaster Recovery as a Cloud Service: Economic Benefits & Deployment Challenges”, 2010, pp. 1-7. |
Yang, et al., “Toward Reliable Data Delivery for Highly Dynamic Mobile Ad Hoc Networks,” 2012, pp. 111-124. |
Yang, et al., “TRAP-Array: A Disk Array Architecture Providing Timely Recovery to Any Point-in-time,” 2006, pp. 1-12. |
Yoshida et al., “Orthros: A High-Reliability Operating System with Transmigration of Processes,” 2013, pp. 318-327. |
Zhao, et al., “Adaptive Distributed Load Balancing Algorithm based on Live Migration of Virtual Machines in Cloud”, 2009, pp. 170-175. |
Zhao, et al., Supporting Application-Tailored Grid File System Sessions with WSRF-Based Services, Advanced Computing and Information Systems Laboratory (ACIS), pp. 24-33. |
International Preliminary Report on Patentability and Written Opinion for PCT/US2011/054374, dated Apr. 2, 2013, 9 pages. |
Number | Date | Country | |
---|---|---|---|
20210357132 A1 | Nov 2021 | US |
Number | Date | Country | |
---|---|---|---|
63025758 | May 2020 | US |