Patent Publication Number: US-2023142346-A1

Title: Data backup in container management frameworks

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to systems, methods, and architectures for data backup in container management frameworks, and in some examples to protecting data clusters in container environments. 
     BACKGROUND 
     The volume and complexity of data that is collected, analyzed, and stored are increasing rapidly over time. The computer infrastructure used to handle these challenges is also becoming more complex, with more processing power and more portability. As a result, data management, online security, and data backup are becoming increasingly important. Significant issues include challenges of restoring data content of clusters that manage containerized workload and services, such as containerized applications. 
     SUMMARY 
     In some examples, a cluster protection system comprises at least one processor, and a memory storing instructions which, when executed by the at least one processor, cause the cluster protection system to perform operations in a method of protecting data in a container management framework, the operations comprising, at least: identifying a target cluster or an object in a container management framework; identifying application data and metadata associated with the target cluster or the object; generating a first snapshot of the target cluster or the object, the first snapshot including at least the metadata; storing the first snapshot in offsite cloud storage; generating a second snapshot of the target cluster, the second snapshot including at least the application data; and storing the second snapshot in a persistent volume in onsite storage. 
     In some examples, the first snapshot further comprises resource data. In some examples, a resource to which the resource data relates includes a cluster controller. 
     In some examples, the cluster controller controls a pod of containers. In some examples, the first snapshot further comprises a cluster configuration. In some examples, the first snapshot further comprises a persistent volume claim (PVC). 
     In some examples, the operations further comprise receiving a restore request for the target cluster or the object and, based on the received restore request, restoring the target cluster or the object using the first snapshot and the second snapshot. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Some embodiments are illustrated by way of example and not limitation in the views of the accompanying drawing: 
         FIG.  1 A  is a block diagram illustrating an example networked computing environment in which some embodiments described herein are practiced. 
         FIG.  1 B  is a block diagram illustrating one embodiment of a server in the example networked computing environment of  FIG.  1 A . 
         FIG.  1 C  is a block diagram illustrating one embodiment of a storage appliance in the example networked computing environment of  FIG.  1 A . 
         FIG.  2    is a block diagram illustrating an example cluster of a distributed decentralized database, according to some example embodiments. 
         FIG.  3    is a block diagram of an example component architecture indicating example backup operations. 
         FIG.  4    is a block diagram of an example component architecture indicating example restore operations. 
         FIG.  5    is a flow chart of example operations in a method, according to an example embodiment. 
         FIG.  6    is a block diagram illustrating an example architecture of software, that can be used to implement various embodiments described herein. 
         FIG.  7    illustrates a diagrammatic representation of an example machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies of various embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION 
     The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments of the present disclosure. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of example embodiments. It will be evident, however, to one skilled in the art that the present inventive subject matter may be practiced without these specific details. 
     It will be appreciated that some of the examples disclosed herein are described in the context of virtual machines that are backed up by using base and incremental snapshots, for example. This should not necessarily be regarded as limiting of the disclosures. The disclosures, systems, and methods described herein apply not only to virtual machines of all types that run a file system (for example), but also to networked-attached storage (NAS) devices, physical machines (for example Linux servers), and databases. 
     In some respects, this disclosure relates to systems, methods, and architectures for data backup in container management frameworks, and in some examples to protecting data clusters in container environments. In some respects, virtualization allows better utilization of resources in a physical server and allows better scalability because an application can be added or updated easily, reduces hardware costs, and much more. With virtualization, examples can present a set of physical resources as a cluster of disposable virtual machines (VMs). Each VM is a full machine running all the components, including its own operating system, on top of the virtualized hardware. Containers are similar to VMs, but typically have relaxed isolation properties to share an Operating System (OS) among applications. Therefore, containers may be considered lightweight. Similar to a VM, a container has its own filesystem, share of CPU, memory, process space, and more. As they are decoupled from the underlying infrastructure, they are portable across clouds and OS distributions. 
     On-disk files in a container, however, may be considered “ephemeral” in the sense that they may vaporize or be destroyed in certain situations, such as when a container crashes or is corrupted. This can present problems for non-trivial applications when running in containers. In some instances, a container can be restarted but it does so with a clean state. A second problem can occur when sharing files between containers running together in a pod. In some examples, a pod is the smallest deployable unit of computing that can be created and managed in a containerized cluster. In some examples, a pod is a group of one or more containers, with shared storage and network resources, and a specification for how to run the containers. In some examples, the contents of a pod are co-located and co-scheduled, and run in a shared context. In various examples, a pod models an application-specific logical host in the sense that it contains one or more application containers which are relatively tightly coupled. In some examples, a pod comprises several containers and is also ephemeral in the sense that it can be destroyed and re-created at any time. In non-cloud contexts, applications executed on the same physical or virtual machine are analogous to cloud applications executed on the same logical host. 
     These problems are addressed in some examples of this disclosure by protecting ephemeral or unprotected cluster data. In some examples, data protection includes two backup phases. In a first phase, various examples store a first “resource” snapshot of a cluster into offsite cloud storage and, in a second phase, store a second “persistent” snapshot in onsite storage. In some examples, the cluster may include, or not include an onsite Kubernetes cluster. In some examples, the first snapshot may include metadata objects, resource data, and configurations, available or identified through certain APIs, for example. Example resources are discussed below. In the second phase, unprotected or so-called ephemeral data, such as application data for example, is backed up and stored in the second snapshot in a persistent volume residing locally onsite. Other types of ephemeral data are possible. The two-phase operations are enabled by various examples of an architecture and data protection system described below. 
     Example technical advantages and/or solutions provided by the two-phase approach, or “splitting” of the snapshots between offsite and onsite storage, may include lower costs and reduction of bandwidth, a leveraging of storage systems, design consistency, a more efficient processing of container libraries in cloud environments, and a reduction in onsite operational costs and load on resources. Additionally, some two-phase examples enable data protection systems that are able to monitor or provide an overview of disparate pods of clusters, such as Kubernetes clusters, and allow restoration of resources across clusters. Some two-phase examples allow a temporary increase in compute resources to conduct operations such as analyzing contents of a snapshot for ransomware detection, capacity planning, and so forth. Some two-phase examples allow data protection systems to include legacy onsite systems to provide continuous data protection, yet enable managed access to a cloud system for an overview of protected clusters. 
     In some examples, a persistent volume (PV) is a data volume that can be mounted by containers, for example in Kubernetes. Other container options are possible. In some examples, a PV is backed up or protected by persistent storage that does not disappear or vaporize i.e., the storage persists when a pod is destroyed or corrupted for example. In some examples, a persistent volume claim (PVC) is a specific binding between a PV and a pod. A PVC may be ephemeral. In some examples, a controller is a resource that can control the state of a given cluster, for example a Kubernetes or containerized cluster. In some examples, a container management framework, such as Kubernetes, is a declarative framework and controllers ensure that a cluster state approximates a desired declared state. For example, a controller can create an appropriate number and/or configuration of pods to deploy and maintain a given application. In some examples, a container storage interface (CSI) implements a storage driver, for example a third-party driver, and allows auto-provisioning of volumes and the taking and/or mounting of snapshots. In some examples, a namespaced resource is a resource that belongs to a specific namespace. Example namespaced resources include pods and PVCs. In some examples, a cluster-scoped resource is a cluster resource that does not belong to a specific namespace. Example cluster-scoped resources may include nodes, storage classes and PVs. In some examples, even though a PV may be cluster-scoped, it may be claimed by a single namespaced PVC at a given time. It can however also remain unclaimed in some examples. 
     Reference will now be made in detail to embodiments of the present disclosure, examples of which are illustrated in the appended drawings. The present disclosure may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. 
       FIG.  1 A  is a block diagram illustrating one embodiment of a networked computing environment  100  in which some embodiments are practiced. As depicted, the networked computing environment  100  includes a data center  150 , a storage appliance  140 , and a computing device  154  in communication with each other via one or more networks  180 . The networked computing environment  100  may include a plurality of computing devices interconnected through one or more networks  180 . The one or more networks  180  may allow computing devices and/or storage devices to connect to and communicate with other computing devices and/or other storage devices. In some cases, the networked computing environment may include other computing devices and/or other storage devices not shown. The other computing devices may include, for example, a mobile computing device, a non-mobile computing device, a server, a workstation, a laptop computer, a tablet computer, a desktop computer, or an information processing system. The other storage devices may include, for example, a storage area network storage device, a networked-attached storage device, a hard disk drive, a SSD, or a data storage system. 
     The networked computing environment  100  may provide a cloud computing environment for one or more computing devices. Cloud computing may refer to Internet-based computing, wherein shared resources, software, and/or information may be provided to one or more computing devices on-demand via the Internet. The networked computing environment  100  may comprise a cloud computing environment (e.g., SaaS platform  318  described below) providing Software-as-a-Service (SaaS) or Infrastructure-as-a-Service (IaaS) services. SaaS may refer to a software distribution model in which applications are hosted by a service provider and made available to users over the Internet. 
     The data center  150  may include one or more servers, such as server  160 , in communication with one or more storage devices, such as storage device  156 . The one or more servers may also be in communication with one or more storage appliances, such as storage appliance  170 . The server  160 , storage device  156 , and storage appliance  170  may be in communication with each other via a networking fabric connecting servers and data storage units within the data center to each other. The storage appliance  170  may include a data management system for backing up virtual machines and/or files within a virtualized infrastructure. The server  160  may be used to create and manage one or more virtual machines associated with a virtualized infrastructure. The one or more virtual machines may run various applications, such as a cloud-based service, a database application, or a web server. The storage device  156  may include one or more hardware storage devices for storing data, such as a hard disk drive (HDD), a magnetic tape drive, a SSD, a storage area network (SAN) storage device, a NAS device, or an offsite cloud storage. In some cases, a data center, such as data center  150 , may include thousands of servers and/or data storage devices in communication with each other. The data storage devices may comprise a tiered data storage infrastructure (or a portion of a tiered data storage infrastructure). The tiered data storage infrastructure may allow for the movement of data across different tiers of a data storage infrastructure between higher-cost, higher-performance storage devices (e.g., solid-state drives and hard disk drives) and relatively lower-cost, lower-performance storage devices (e.g., magnetic tape drives). 
     The one or more networks  180  may include a secure network such as an enterprise private network, an unsecure network such as a wireless open network, a local area network (LAN), a wide area network (WAN), and the Internet. The one or more networks  180  may include a cellular network, a mobile network, a wireless network, or a wired network. Each network of the one or more networks  180  may include hubs, bridges, routers, switches, and wired transmission media such as a direct-wired connection. The one or more networks  180  may include an extranet or other private network for securely sharing information or providing controlled access to applications or files. 
     A server, such as server  160 , may allow a client to download information or files (e.g., executable, text, application, audio, image, or video files) from the server  160  or to perform a search query related to particular information stored on the server  160 . In some cases, a server may act as an application server or a file server. In general, a server may refer to a hardware device that acts as the host in a client-server relationship or a software process that shares a resource with or performs work for one or more clients. 
     One embodiment of server  160  includes a network interface  165 , processor  166 , memory  167 , disk  168 , and virtualization manager  169  all in communication with each other. Network interface  165  allows server  160  to connect to one or more networks  180 . Network interface  165  may include a wireless network interface and/or a wired network interface. Processor  166  allows server  160  to execute computer-readable instructions stored in memory  167 . Processor  166  may include one or more processing units or processing devices, such as one or more CPUs and/or one or more GPUs. Memory  167  may comprise one or more types of memory (e.g., RAM, SRAM, DRAM, ROM, EEPROM, Flash, etc.). Disk  168  may include a hard disk drive and/or a solid-state drive. Memory  167  and disk  168  may comprise hardware storage devices. 
     The virtualization manager  169  may manage a virtualized infrastructure and perform management operations associated with the virtualized infrastructure. The virtualization manager  169  may manage the provisioning of virtual machines running within the virtualized infrastructure and provide an interface to computing devices interacting with the virtualized infrastructure. In one example, the virtualization manager  169  may set a virtual machine into a frozen state in response to a snapshot request made via an application programming interface (API) by a storage appliance, such as storage appliance  170 . Setting the virtual machine into a frozen state may allow a point-in-time snapshot of the virtual machine to be stored or transferred. In one example, updates made to a virtual machine that has been set into a frozen state may be written to a separate file (e.g., an update file) while the virtual disk file associated with the state of the virtual disk at the point in time is frozen. The virtual disk file may be set into a read-only state to prevent modifications to the virtual disk file while the virtual machine is in the frozen state. The virtualization manager  169  may then transfer data associated with the virtual machine (e.g., an image of the virtual machine or a portion of the image of the virtual machine) to a storage appliance in response to a request made by the storage appliance. After the data associated with the point-in-time snapshot of the virtual machine has been transferred to the storage appliance, the virtual machine may be released from the frozen state (i.e., unfrozen) and the updates made to the virtual machine and stored in the separate file may be merged into the virtual disk file. The virtualization manager  169  may perform various virtual machine-related tasks, such as cloning virtual machines, creating new virtual machines, monitoring the state of virtual machines, moving virtual machines between physical hosts for load balancing purposes, and facilitating backups of virtual machines. 
     One embodiment of storage appliance  170  includes a network interface  175 , processor  176 , memory  177 , and disk  178  all in communication with each other. Network interface  175  allows storage appliance  170  to connect to one or more networks  180 . Network interface  175  may include a wireless network interface and/or a wired network interface. Processor  176  allows storage appliance  170  to execute computer-readable instructions stored in memory  177 . Processor  176  may include one or more processing units, such as one or more CPUs and/or one or more GPUs. Memory  177  may comprise one or more types of memory (e.g., RAM, SRAM, DRAM, ROM, EEPROM, NOR Flash, NAND Flash, etc.). Disk  178  may include a hard disk drive and/or a solid-state drive. Memory  177  and disk  178  may comprise hardware storage devices. 
     In one embodiment, the storage appliance  170  may include four machines. Each of the four machines may include a multi-core CPU, 64 GB of RAM, a 400 GB SSD, three 4 TB HDDs, and a network interface controller. In this case, the four machines may be in communication with the one or more networks  180  via the four network interface controllers. The four machines may comprise four nodes of a server cluster. The server cluster may comprise a set of physical machines that are connected together via a network. The server cluster may be used for storing data associated with a plurality of virtual machines, such as backup data associated with different point-in-time versions of 1000 virtual machines. 
     The networked computing environment  100  may provide a cloud computing environment for one or more computing devices. Cloud computing may refer to Internet-based computing, wherein shared resources, software, and/or information may be provided to one or more computing devices on-demand via the Internet. The networked computing environment  100  may comprise a cloud computing environment providing Software-as-a-Service (SaaS) or Infrastructure-as-a-Service (IaaS) services. SaaS may refer to a software distribution model in which applications are hosted by a service provider and made available to end users over the Internet. In one embodiment, the networked computing environment  100  may include a virtualized infrastructure that provides software, data processing, and/or data storage services to end users accessing the services via the networked computing environment. In one example, networked computing environment  100  may provide cloud-based work productivity or business-related applications to a computing device, such as computing device  154 . The storage appliance  140  may comprise a cloud-based data management system for backing up virtual machines and/or files within a virtualized infrastructure, such as virtual machines running on server  160  or files stored on server  160 . 
     In some cases, networked computing environment  100  may provide remote access to secure applications and files stored within data center  150  from a remote computing device, such as computing device  154 . The data center  150  may use an access control application to manage remote access to protected resources, such as protected applications, databases, or files located within the data center  150 . To facilitate remote access to secure applications and files, a secure network connection may be established using a virtual private network (VPN). A VPN connection may allow a remote computing device, such as computing device  154 , to securely access data from a private network (e.g., from a company file server or mail server) using an unsecure public network or the Internet. The VPN connection may require client-side software (e.g., running on the remote computing device) to establish and maintain the VPN connection. The VPN client software may provide data encryption and encapsulation prior to the transmission of secure private network traffic through the Internet. 
     In some embodiments, the storage appliance  170  may manage the extraction and storage of virtual machine snapshots associated with different point-in-time versions of one or more virtual machines running within the data center  150 . A snapshot of a virtual machine may correspond with a state of the virtual machine at a particular point in time. In response to a restore command from the server  160 , the storage appliance  170  may restore a point-in-time version of a virtual machine or restore point-in-time versions of one or more files located on the virtual machine and transmit the restored data to the server  160 . In response to a mount command from the server  160 , the storage appliance  170  may allow a point-in-time version of a virtual machine to be mounted and allow the server  160  to read and/or modify data associated with the point-in-time version of the virtual machine. To improve storage density, the storage appliance  170  may deduplicate and compress data associated with different versions of a virtual machine and/or deduplicate and compress data associated with different virtual machines. To improve system performance, the storage appliance  170  may first store virtual machine snapshots received from a virtualized environment in a cache, such as a flash-based cache. The cache may also store popular data or frequently accessed data (e.g., based on a history of virtual machine restorations, incremental files associated with commonly restored virtual machine versions) and current day incremental files or incremental files corresponding with snapshots captured within the past 24 hours. 
     An incremental file may comprise a forward incremental file or a reverse incremental file. A forward incremental file may include a set of data representing changes that have occurred since an earlier point-in-time snapshot of a virtual machine. To generate a snapshot of the virtual machine corresponding with a forward incremental file, the forward incremental file may be combined with an earlier point-in-time snapshot of the virtual machine (e.g., the forward incremental file may be combined with the last full image of the virtual machine that was captured before the forward incremental was captured and any other forward incremental files that were captured subsequent to the last full image and prior to the forward incremental file). A reverse incremental file may include a set of data representing changes from a later point-in-time snapshot of a virtual machine. To generate a snapshot of the virtual machine corresponding with a reverse incremental file, the reverse incremental file may be combined with a later point-in-time snapshot of the virtual machine (e.g., the reverse incremental file may be combined with the most recent snapshot of the virtual machine and any other reverse incremental files that were captured prior to the most recent snapshot and subsequent to the reverse incremental file). 
     The storage appliance  170  may provide a user interface (e.g., a web-based interface or a graphical user interface (GUI)) that displays virtual machine backup information such as identifications of the virtual machines protected and the historical versions or time machine views for each of the virtual machines protected. A time machine view of a virtual machine may include snapshots of the virtual machine over a plurality of points in time. Each snapshot may comprise the state of the virtual machine at a particular point in time. Each snapshot may correspond with a different version of the virtual machine (e.g., Version 1 of a virtual machine may correspond with the state of the virtual machine at a first point in time and Version 2 of the virtual machine may correspond with the state of the virtual machine at a second point in time subsequent to the first point in time). 
     The user interface may enable an end user of the storage appliance  170  (e.g., a system administrator or a virtualization administrator) to select a particular version of a virtual machine to be restored or mounted. When a particular version of a virtual machine has been mounted, the particular version may be accessed by a client (e.g., a virtual machine, a physical machine, or a computing device) as if the particular version was local to the client. A mounted version of a virtual machine may correspond with a mount point directory (e.g., /snapshots/VM5/Version23). In one example, the storage appliance  170  may run an NFS server and make the particular version (or a copy of the particular version) of the virtual machine accessible for reading and/or writing. The end user of the storage appliance  170  may then select the particular version to be mounted and run an application (e.g., a data analytics application) using the mounted version of the virtual machine. In another example, the particular version may be mounted as an iSCSI target. 
       FIG.  1 B  is a block diagram illustrating one embodiment of server  160  in  FIG.  1 A . The server  160  may comprise one server out of a plurality of servers that are networked together within a data center, such as the data center  150 . In one example, the plurality of servers may be positioned within one or more server racks within the data center. As depicted, the server  160  includes hardware-level components and software-level components. The hardware-level components include one or more processors  182 , one or more memory  184 , and one or more disks  185 . The software-level components include a hypervisor  186 , a virtualized infrastructure manager  199 , and one or more virtual machines, such as virtual machine  198 . The hypervisor  186  may comprise a native hypervisor or a hosted hypervisor. The hypervisor  186  may provide a virtual operating platform for running one or more virtual machines, such as virtual machine  198 . Virtual machine  198  includes a plurality of virtual hardware devices including a virtual processor  192 , a virtual memory  194 , and a virtual disk  195 . The virtual disk  195  may comprise a file stored within the one or more disks  185 . In one example, a virtual machine may include a plurality of virtual disks, with each virtual disk of the plurality of virtual disks associated with a different file stored on the one or more disks  185 . Virtual machine  198  may include a guest operating system  196  that runs one or more applications, such as application  197 . 
     The virtualized infrastructure manager  199 , which may correspond with the virtualization manager  169  in  FIG.  1 A , may run on a virtual machine or natively on the server  160 . The virtualized infrastructure manager  199  may provide a centralized platform for managing a virtualized infrastructure that includes a plurality of virtual machines. The virtualized infrastructure manager  199  may manage the provisioning of virtual machines running within the virtualized infrastructure and provide an interface to computing devices interacting with the virtualized infrastructure. The virtualized infrastructure manager  199  may perform various virtualized infrastructure related tasks, such as cloning virtual machines, creating new virtual machines, monitoring the state of virtual machines, and facilitating backups of virtual machines. 
     In one embodiment, the server  160  may use the virtualized infrastructure manager  199  to facilitate backups for a plurality of virtual machines (e.g., eight different virtual machines) running on the server  160 . Each virtual machine running on the server  160  may run its own guest operating system and its own set of applications. Each virtual machine running on the server  160  may store its own set of files using one or more virtual disks associated with the virtual machine (e.g., each virtual machine may include two virtual disks that are used for storing data associated with the virtual machine). 
     In one embodiment, a data management application running on a storage appliance, such as storage appliance  140  in  FIG.  1 A  or storage appliance  170  in  FIG.  1 A , may request a snapshot of a virtual machine running on server  160 . The snapshot of the virtual machine may be stored as one or more files, with each file associated with a virtual disk of the virtual machine. A snapshot of a virtual machine may correspond with a state of the virtual machine at a particular point in time. The particular point in time may be associated with a time stamp. In one example, a first snapshot of a virtual machine may correspond with a first state of the virtual machine (including the state of applications and files stored on the virtual machine) at a first point in time (e.g., 5:30 p.m. on Jun. 29, 2024) and a second snapshot of the virtual machine may correspond with a second state of the virtual machine at a second point in time subsequent to the first point in time (e.g., 5:30 p.m. on Jun. 30, 2024). 
     In response to a request for a snapshot of a virtual machine at a particular point in time, the virtualized infrastructure manager  199  may set the virtual machine into a frozen state or store a copy of the virtual machine at the particular point in time. The virtualized infrastructure manager  199  may then transfer data associated with the virtual machine (e.g., an image of the virtual machine or a portion of the image of the virtual machine) to the storage appliance. The data associated with the virtual machine may include a set of files including a virtual disk file storing contents of a virtual disk of the virtual machine at the particular point in time and a virtual machine configuration file storing configuration settings for the virtual machine at the particular point in time. The contents of the virtual disk file may include the operating system used by the virtual machine, local applications stored on the virtual disk, and user files (e.g., images and word processing documents). In some cases, the virtualized infrastructure manager  199  may transfer a full image of the virtual machine to the storage appliance or a plurality of data blocks corresponding with the full image (e.g., to enable a full image-level backup of the virtual machine to be stored on the storage appliance). In other cases, the virtualized infrastructure manager  199  may transfer a portion of an image of the virtual machine associated with data that has changed since an earlier point in time prior to the particular point in time or since a last snapshot of the virtual machine was taken. In one example, the virtualized infrastructure manager  199  may transfer only data associated with virtual blocks stored on a virtual disk of the virtual machine that have changed since the last snapshot of the virtual machine was taken. In one embodiment, the data management application may specify a first point in time and a second point in time and the virtualized infrastructure manager  199  may output one or more virtual data blocks associated with the virtual machine that have been modified between the first point in time and the second point in time. 
     In some embodiments, the server  160  may or the hypervisor  186  may communicate with a storage appliance, such as storage appliance  140  in  FIG.  1 A  or storage appliance  170  in  FIG.  1 A , using a distributed file system protocol such as NFS. The distributed file system protocol may allow the server  160  or the hypervisor  186  to access, read, write, or modify files stored on the storage appliance as if the files were locally stored on the server. The distributed file system protocol may allow the server  160  or the hypervisor  186  to mount a directory or a portion of a file system located within the storage appliance. 
       FIG.  1 C  is a block diagram illustrating one embodiment of storage appliance  170  in  FIG.  1 A . The storage appliance may include a plurality of physical machines that may be grouped together and presented as a single computing system. Each physical machine of the plurality of physical machines may comprise a node in a cluster (e.g., a failover cluster). In one example, the storage appliance may be positioned within a server rack within a data center. As depicted, the storage appliance  170  includes hardware-level components and software-level components. The hardware-level components include one or more physical machines, such as physical machine  120  and physical machine  130 . The physical machine  120  includes a network interface  121 , processor  122 , memory  123 , and disk  124  all in communication with each other. Processor  122  allows physical machine  120  to execute computer-readable instructions stored in memory  123  to perform processes described herein. Disk  124  may include a hard disk drive and/or a solid-state drive. The physical machine  130  includes a network interface  131 , processor  132 , memory  133 , and disk  134  all in communication with each other. Processor  132  allows physical machine  130  to execute computer-readable instructions stored in memory  133  to perform processes described herein. Disk  134  may include an HDD and/or an SSD. In some cases, disk  134  may include a flash-based SSD or a hybrid HDD/SSD drive. In one embodiment, the storage appliance  170  may include a plurality of physical machines arranged in a cluster (e.g., eight machines in a cluster). Each of the plurality of physical machines may include a plurality of multi-core CPUs, 128 GB of RAM, a 500 GB SSD, four 4 TB HDDs, and a network interface controller. 
     In some embodiments, the plurality of physical machines may be used to implement a cluster-based network file server. The cluster-based network file server may neither require nor use a front-end load balancer. One issue with using a front-end load balancer to host the IP address for the cluster-based network file server and to forward requests to the nodes of the cluster-based network file server is that the front-end load balancer comprises a single point of failure for the cluster-based network file server. In some cases, the file system protocol used by a server, such as server  160  in  FIG.  1 A , or a hypervisor, such as hypervisor  186  in  FIG.  1 B , to communicate with the storage appliance  170  may not provide a failover mechanism (e.g., NFS Version 3). In the case that no failover mechanism is provided on the client-side, the hypervisor may not be able to connect to a new node within a cluster in the event that the node connected to the hypervisor fails. 
     In some embodiments, each node in a cluster may be connected to each other via a network and may be associated with one or more IP addresses (e.g., two different IP addresses may be assigned to each node). In one example, each node in the cluster may be assigned a permanent IP address and a floating IP address and may be accessed using either the permanent IP address or the floating IP address. In this case, a hypervisor, such as hypervisor  186  in  FIG.  1 B , may be configured with a first floating IP address associated with a first node in the cluster. The hypervisor may connect to the cluster using the first floating IP address. In one example, the hypervisor may communicate with the cluster using the NFS Version 3 protocol. Each node in the cluster may run a Virtual Router Redundancy Protocol (VRRP) daemon. A daemon may comprise a background process. Each VRRP daemon may include a list of all floating IP addresses available within the cluster. In the event that the first node associated with the first floating IP address fails, one of the VRRP daemons may automatically assume or pick up the first floating IP address if no other VRRP daemon has already assumed the first floating IP address. Therefore, if the first node in the cluster fails or otherwise goes down, then one of the remaining VRRP daemons running on the other nodes in the cluster may assume the first floating IP address that is used by the hypervisor for communicating with the cluster. 
     In order to determine which of the other nodes in the cluster will assume the first floating IP address, a VRRP priority may be established. In one example, given a number (N) of nodes in a cluster from node( 0 ) to node(N−1), for a floating IP address (i), the VRRP priority of node(j) may be (j-i) modulo N. In another example, given a number (N) of nodes in a cluster from node( 0 ) to node(N−1), for a floating IP address (i), the VRRP priority of node(j) may be (i-j) modulo N. In these cases, node(j) will assume floating IP address (i) only if its VRRP priority is higher than that of any other node in the cluster that is alive and announcing itself on the network. Thus, if a node fails, then there may be a clear priority ordering for determining which other node in the cluster will take over the failed node&#39;s floating IP address. 
     In some cases, a cluster may include a plurality of nodes and each node of the plurality of nodes may be assigned a different floating IP address. In this case, a first hypervisor may be configured with a first floating IP address associated with a first node in the cluster, a second hypervisor may be configured with a second floating IP address associated with a second node in the cluster, and a third hypervisor may be configured with a third floating IP address associated with a third node in the cluster. 
     As depicted in  FIG.  1 C , the software-level components of the storage appliance  170  may include data management system  102 , a virtualization interface  104 , a distributed job scheduler  108 , a distributed metadata store  110 , a distributed file system  112 , and one or more virtual machine search indexes, such as virtual machine search index  106 . In one embodiment, the software-level components of the storage appliance  170  may be run using a dedicated hardware-based appliance. In another embodiment, the software-level components of the storage appliance  170  may be run from the cloud (e.g., the software-level components may be installed on a cloud service provider). 
     In some cases, the data storage across a plurality of nodes in a cluster (e.g., the data storage available from the one or more physical machines) may be aggregated and made available over a single file system namespace (e.g., /snapshots/). A directory for each virtual machine protected using the storage appliance  170  may be created (e.g., the directory for Virtual Machine A may be /snapshots/VM_A). Snapshots and other data associated with a virtual machine may reside within the directory for the virtual machine. In one example, snapshots of a virtual machine may be stored in subdirectories of the directory (e.g., a first snapshot of Virtual Machine A may reside in /snapshots/VM_A/s1/ and a second snapshot of Virtual Machine A may reside in /snapshots/VM_A/s2/). 
     The distributed file system  112  may present itself as a single file system, in which as new physical machines or nodes are added to the storage appliance  170 , the cluster may automatically discover the additional nodes and automatically increase the available capacity of the file system for storing files and other data. Each file stored in the distributed file system  112  may be partitioned into one or more chunks. Each of the one or more chunks may be stored within the distributed file system  112  as a separate file. 
     In some embodiments, the data management system  102  resides inside the distributed file system  112 . The data management system  102  may receive requests to capture snapshots of the entire distributed file system  112 , on a periodic basis based on internal protocols, or upon occurrence of user-triggered events. 
     The files stored within the distributed file system  112  may be replicated or mirrored over a plurality of physical machines, thereby creating a load-balanced and fault tolerant distributed file system. In one example, storage appliance  170  may include ten physical machines arranged as a failover cluster and a first file corresponding with a snapshot of a virtual machine (e.g., /snapshots/VM_A/s1/s1.full) may be replicated and stored on three of the ten machines. 
     The distributed metadata store  110  may include a distributed database management system that provides high availability without a single point of failure. In one embodiment, the distributed metadata store  110  may comprise a database, such as a distributed document-oriented database. The distributed metadata store  110  may be used as a distributed key value storage system. In one example, the distributed metadata store  110  may comprise a distributed NoSQL key value store database. In some cases, the distributed metadata store  110  may include a partitioned row store, in which rows are organized into tables or other collections of related data held within a structured format within the key value store database. A table (or a set of tables) may be used to store metadata information associated with one or more files stored within the distributed file system  112 . The metadata information may include the name of a file, a size of the file, file permissions associated with the file, when the file was last modified, and file mapping information associated with an identification of the location of the file stored within a cluster of physical machines. In one embodiment, a new file corresponding with a snapshot of a virtual machine may be stored within the distributed file system  112  and metadata associated with the new file may be stored within the distributed metadata store  110 . The distributed metadata store  110  may also be used to store a backup schedule for the virtual machine and a list of snapshots for the virtual machine that are stored using the storage appliance  170 . 
     In some cases, the distributed metadata store  110  may be used to manage one or more versions of a virtual machine. Each version of the virtual machine may correspond with a full image snapshot of the virtual machine stored within the distributed file system  112  or an incremental snapshot of the virtual machine (e.g., a forward incremental or reverse incremental) stored within the distributed file system  112 . In one embodiment, the one or more versions of the virtual machine may correspond with a plurality of files. The plurality of files may include a single full-image snapshot of the virtual machine and one or more incremental snapshots derived from the single full-image snapshot. The single full-image snapshot of the virtual machine may be stored using a first storage device of a first type (e.g., an HDD) and the one or more incrementals derived from the single full-image snapshot may be stored using a second storage device of a second type (e.g., an SSD). In this case, only a single full-image needs to be stored and each version of the virtual machine may be generated from the single full image, or the single full image combined with a subset of the one or more incrementals. Furthermore, each version of the virtual machine may be generated by performing a sequential read from the first storage device (e.g., reading a single file from an HDD) to acquire the full image and, in parallel, performing one or more reads from the second storage device (e.g., performing fast random reads from an SSD) to acquire the one or more incrementals. 
     The distributed job scheduler  108  may be used for scheduling backup jobs that acquire and store virtual machine snapshots for one or more virtual machines over time. The distributed job scheduler  108  may follow a backup schedule to backup an entire image of a virtual machine at a particular point in time or one or more virtual disks associated with the virtual machine at the particular point in time. In one example, the backup schedule may specify that the virtual machine be backed up at a snapshot capture frequency, such as every two hours or every 24 hours. Each backup job may be associated with one or more tasks to be performed in a sequence. Each of the one or more tasks associated with a job may be run on a particular node within a cluster. In some cases, the distributed job scheduler  108  may schedule a specific job to be run on a particular node based on data stored on the particular node. For example, the distributed job scheduler  108  may schedule a virtual machine snapshot job to be run on anode in a cluster that is used to store snapshots of the virtual machine in order to reduce network congestion. 
     The distributed job scheduler  108  may comprise a distributed fault-tolerant job scheduler, in which jobs affected by node failures are recovered and rescheduled to be run on available nodes. In one embodiment, the distributed job scheduler  108  may be fully decentralized and implemented without the existence of a master node. The distributed job scheduler  108  may run job scheduling processes on each node in a cluster or on a plurality of nodes in the cluster. In one example, the distributed job scheduler  108  may run a first set of job scheduling processes on a first node in the cluster, a second set of job scheduling processes on a second node in the cluster, and a third set of job scheduling processes on a third node in the cluster. The first set of job scheduling processes, the second set of job scheduling processes, and the third set of job scheduling processes may store information regarding jobs, schedules, and the states of jobs using a metadata store, such as distributed metadata store  110 . In the event that the first node running the first set of job scheduling processes fails (e.g., due to a network failure or a physical machine failure), the states of the jobs managed by the first set of job scheduling processes may fail to be updated within a threshold period of time (e.g., a job may fail to be completed within 30 seconds or within 3 minutes from being started). In response to detecting jobs that have failed to be updated within the threshold period of time, the distributed job scheduler  108  may undo and restart the failed jobs on available nodes within the cluster. 
     The job scheduling processes running on at least a plurality of nodes in a cluster (e.g., on each available node in the cluster) may manage the scheduling and execution of a plurality of jobs. The job scheduling processes may include run processes for running jobs, cleanup processes for cleaning up failed tasks, and rollback processes for rolling-back or undoing any actions or tasks performed by failed jobs. In one embodiment, the job scheduling processes may detect that a particular task for a particular job has failed and in response may perform a cleanup process to clean up or remove the effects of the particular task and then perform a rollback process that processes one or more completed tasks for the particular job in reverse order to undo the effects of the one or more completed tasks. Once the particular job with the failed task has been undone, the job scheduling processes may restart the particular job on an available node in the cluster. 
     The distributed job scheduler  108  may manage a job in which a series of tasks associated with the job are to be performed atomically (i.e., partial execution of the series of tasks is not permitted). If the series of tasks cannot be completely executed or there is any failure that occurs to one of the series of tasks during execution (e.g., a hard disk associated with a physical machine fails or a network connection to the physical machine fails), then the state of a data management system may be returned to a state as if none of the series of tasks were ever performed. The series of tasks may correspond with an ordering of tasks for the series of tasks and the distributed job scheduler  108  may ensure that each task of the series of tasks is executed based on the ordering of tasks. Tasks that do not have dependencies with each other may be executed in parallel. 
     In some cases, the distributed job scheduler  108  may schedule each task of a series of tasks to be performed on a specific node in a cluster. In other cases, the distributed job scheduler  108  may schedule a first task of the series of tasks to be performed on a first node in a cluster and a second task of the series of tasks to be performed on a second node in the cluster. In these cases, the first task may have to operate on a first set of data (e.g., a first file stored in a file system) stored on the first node and the second task may have to operate on a second set of data (e.g., metadata related to the first file that is stored in a database) stored on the second node. In some embodiments, one or more tasks associated with a job may have an affinity to a specific node in a cluster. In one example, if the one or more tasks require access to a database that has been replicated on three nodes in a cluster, then the one or more tasks may be executed on one of the three nodes. In another example, if the one or more tasks require access to multiple chunks of data associated with a virtual disk that has been replicated over four nodes in a cluster, then the one or more tasks may be executed on one of the four nodes. Thus, the distributed job scheduler  108  may assign one or more tasks associated with a job to be executed on a particular node in a cluster based on the location of data to be accessed by the one or more tasks. 
     In one embodiment, the distributed job scheduler  108  may manage a first job associated with capturing and storing a snapshot of a virtual machine periodically (e.g., every 30 minutes). The first job may include one or more tasks, such as communicating with a virtualized infrastructure manager, such as the virtualized infrastructure manager  199  in  FIG.  1 B , to create a frozen copy of the virtual machine and to transfer one or more chunks (or one or more files) associated with the frozen copy to a storage appliance, such as storage appliance  170  in  FIG.  1 A . The one or more tasks may also include generating metadata for the one or more chunks, storing the metadata using the distributed metadata store  110 , storing the one or more chunks within the distributed file system  112 , and communicating with the virtualized infrastructure manager  199  that the frozen copy of the virtual machine may be unfrozen or released from a frozen state. The metadata for a first chunk of the one or more chunks may include information specifying a version of the virtual machine associated with the frozen copy, a time associated with the version (e.g., the snapshot of the virtual machine was taken at 5:30 p.m. on Jun. 29, 2024), and a file path to where the first chunk is stored within the distributed file system  112  (e.g., the first chunk is located at /snapshots/VM_B/s1/s1.chunk1). The one or more tasks may also include deduplication, compression (e.g., using a lossless data compression algorithm such as LZ4 or LZ77), decompression, encryption (e.g., using a symmetric key algorithm such as Triple DES or AES-256), and decryption-related tasks. 
     The virtualization interface  104  may provide an interface for communicating with a virtualized infrastructure manager managing a virtualization infrastructure, such as virtualized infrastructure manager  199  in  FIG.  1 B , and requesting data associated with virtual machine snapshots from the virtualization infrastructure. The virtualization interface  104  may communicate with the virtualized infrastructure manager using an API for accessing the virtualized infrastructure manager (e.g., to communicate a request for a snapshot of a virtual machine). In this case, storage appliance  170  may request and receive data from a virtualized infrastructure without requiring agent software to be installed or running on virtual machines within the virtualized infrastructure. The virtualization interface  104  may request data associated with virtual blocks stored on a virtual disk of the virtual machine that have changed since a last snapshot of the virtual machine was taken or since a specified prior point in time. Therefore, in some cases, if a snapshot of a virtual machine is the first snapshot taken of the virtual machine, then a full image of the virtual machine may be transferred to the storage appliance. However, if the snapshot of the virtual machine is not the first snapshot taken of the virtual machine, then only the data blocks of the virtual machine that have changed since a prior snapshot was taken may be transferred to the storage appliance. 
     The virtual machine search index  106  may include a list of files that have been stored using a virtual machine and a version history for each of the files in the list. Each version of a file may be mapped to the earliest point-in-time snapshot of the virtual machine that includes the version of the file or to a snapshot of the virtual machine that includes the version of the file (e.g., the latest point-in-time snapshot of the virtual machine that includes the version of the file). In one example, the virtual machine search index  106  may be used to identify a version of the virtual machine that includes a particular version of a file (e.g., a particular version of a database, a spreadsheet, or a word processing document). In some cases, each of the virtual machines that are backed up or protected using storage appliance  170  may have a corresponding virtual machine search index. 
     In one embodiment, as each snapshot of a virtual machine is ingested, each virtual disk associated with the virtual machine is parsed in order to identify a file system type associated with the virtual disk and to extract metadata (e.g., file system metadata) for each file stored on the virtual disk. The metadata may include information for locating and retrieving each file from the virtual disk. The metadata may also include a name of a file, the size of the file, the last time at which the file was modified, and a content checksum for the file. Each file that has been added, deleted, or modified since a previous snapshot was captured may be determined using the metadata (e.g., by comparing the time at which a file was last modified with a time associated with the previous snapshot). Thus, for every file that has existed within any of the snapshots of the virtual machine, a virtual machine search index may be used to identify when the file was first created (e.g., corresponding with a first version of the file) and at what times the file was modified (e.g., corresponding with subsequent versions of the file). Each version of the file may be mapped to a particular version of the virtual machine that stores that version of the file. 
     In some cases, if a virtual machine includes a plurality of virtual disks, then a virtual machine search index may be generated for each virtual disk of the plurality of virtual disks. For example, a first virtual machine search index may catalog and map files located on a first virtual disk of the plurality of virtual disks and a second virtual machine search index may catalog and map files located on a second virtual disk of the plurality of virtual disks. In this case, a global file catalog or a global virtual machine search index for the virtual machine may include the first virtual machine search index and the second virtual machine search index. A global file catalog may be stored for each virtual machine backed up by a storage appliance within a file system, such as distributed file system  112  in  FIG.  1 C . 
     The data management system  102  may comprise an application running on the storage appliance that manages and stores one or more snapshots of a virtual machine. In one example, the data management system  102  may comprise the highest-level layer in an integrated software stack running on the storage appliance. The integrated software stack may include the data management system  102 , the virtualization interface  104 , the distributed job scheduler  108 , the distributed metadata store  110 , and the distributed file system  112 . In some cases, the integrated software stack may run on other computing devices, such as a server or computing device  154  in  FIG.  1 A . The data management system  102  may use the virtualization interface  104 , the distributed job scheduler  108 , the distributed metadata store  110 , and the distributed file system  112  to manage and store one or more snapshots of a virtual machine. Each snapshot of the virtual machine may correspond with a point-in-time version of the virtual machine. The data management system  102  may generate and manage a list of versions for the virtual machine. Each version of the virtual machine may map to or reference one or more chunks and/or one or more files stored within the distributed file system  112 . Combined together, the one or more chunks and/or the one or more files stored within the distributed file system  112  may comprise a full image of the version of the virtual machine. 
     In some examples, one or more components or operations of the data center  150  or the data management system  102  form part of or comprise a cloud data management (CDM) system. The cloud data management system may provide an onsite service at a datacenter or client, for example. In some embodiments, a cloud management system provides management of one or more clusters of nodes as described herein, such as management of one or more policies with respect to the one or more clusters of nodes. The cloud data management system can serve as a cluster manager for one or more clusters of nodes (e.g., present in the networked computing environment  100 ), for examples one or more Kubernetes clusters in a containerized environment, as described herein. According to various embodiments, the cloud data management system provides for central management of policies (e.g., SLAs) that remotely manages and synchronizes policy definitions with clusters of nodes. For some embodiments, the cloud data management system facilitates automatic setup of secure communication channels between clusters of nodes to facilitate replication of data. Additionally, for some embodiments, the cloud data management system manages archival settings for one or more clusters of nodes with respect to cloud-based data storage resource provided by one or more cloud service provider. Some examples of a data management system, such as the data management system  102  above, include CDM and backup capabilities. Examples below are described in that context. Some examples include onsite data management and backup and other capabilities. 
       FIG.  2    is a block diagram illustrating an example cluster  200  of a distributed decentralized database, according to some example embodiments. As illustrated, the example cluster  200  includes five nodes, nodes  1 - 5 . In some example embodiments, each of the five nodes runs from different machines, such as physical machine  130  in  FIG.  1 C  or virtual machine  198  in  FIG.  1 B . The nodes in the example cluster  200  can include instances of peer nodes of a distributed database (e.g., cluster-based database, distributed decentralized database management system, an SQL database, a NoSQL database, Apache Cassandra, DataStax, MongoDB, CouchDB), according to some example embodiments. In a containerized management framework, the nodes  1 - 5  may be included in a Kubernetes cluster, for example. In various examples, the distributed database system is distributed in that data is sharded or distributed across the example cluster  200  in shards or chunks and decentralized in that there is no central storage device and no single point of failure. The system operates under an assumption that multiple nodes may go down, up, become non-responsive, and so on. Sharding is splitting up of the data horizontally and managing each shard separately on different nodes. For example, if the data managed by the example cluster  200  can be indexed using the  26  letters of the alphabet, node  1  can manage a first shard that handles records that start with A through E, node  2  can manage a second shard that handles records that start with F through J, and so on. 
     In some example embodiments, data written to one of the nodes is replicated to one or more other nodes per a replication protocol of the example cluster  200 . For example, data written to node  1  can be replicated to nodes  2  and  3 . If node  1  prematurely terminates, node  2  and/or  3  can be used to provide the replicated data. In some example embodiments, each node of example cluster  200  frequently exchanges state information about itself and other nodes across the example cluster  200  using gossip protocol. Gossip protocol is a peer-to-peer communication protocol in which each node randomly shares (e.g., communicates, requests, transmits) location and state information about the other nodes in a given cluster. 
     Writing: For a given node, a sequentially written commit log captures the write activity to ensure data durability. The data is then written to an in-memory structure (e.g., a memtable, write-back cache). Each time the in-memory structure is full, the data is written to disk in a Sorted String Table data file. In some example embodiments, writes are automatically partitioned and replicated throughout the example cluster  200 . 
     Reading: Any node of example cluster  200  can receive a read request (e.g., query) from an external client. If the node that receives the read request manages the data requested, the node provides the requested data. If the node does not manage the data, the node determines which node manages the requested data. The node that received the read request then acts as a proxy between the requesting entity and the node that manages the data (e.g., the node that manages the data sends the data to the proxy node, which then provides the data to an external entity that generated the request). 
     The distributed decentralized database system is decentralized in that there is no single point of failure due to the nodes being symmetrical and seamlessly replaceable. For example, whereas conventional distributed data implementations have nodes with different functions (e.g., master/slave nodes, asymmetrical database nodes, federated databases), the nodes of example cluster  200  are configured to function the same way (e.g., as symmetrical peer database nodes that communicate via gossip protocol, such as Cassandra nodes) with no single point of failure. If one of the nodes in example cluster  200  terminates prematurely (“goes down”), another node can rapidly take the place of the terminated node without disrupting service. The example cluster  200  can be a container for a keyspace, which is a container for data in the distributed decentralized database system (e.g., whereas a database is a container for containers in conventional relational databases, the Cassandra keyspace is a container for a Cassandra database system). 
     With reference to  FIG.  3   , an example component architecture  302  is shown. The components include a client&#39;s onsite compute environment or network  304 . In some examples, the client compute environment  304  includes a container management framework. In the illustrated example, the client compute environment  304  includes one or more data clusters  306  (for example Kubernetes clusters), and a cluster agent  308 . 
     The client compute environment  304  further includes an onsite data management system, for example the data management system  102  of  FIG.  1 C . The data management system  102  may have CDM capabilities as discussed above. In various examples, the data management system  102  includes onsite storage  312 . The onsite storage  312  includes one or more PVs. In various examples, one or more of the client compute environment  304 , the cluster agent  308 , and the data management system  102  has access to offsite cloud storage  314 . 
     In some examples, the component architecture  302  includes a cluster protection system  316 . The cluster protection system protects data on a cluster, such as the clusters  306 . The cluster protection system  316  may reside onsite or offsite in communication with the client compute environment  304 , and more particularly in communication with the cluster agent  308  and the data management system  310  thereof. 
     In some examples, the cluster protection system  316  resides in a SaaS platform  318 . In various embodiments, the SaaS platform  318  communicates directly with the client compute environment  304 . The SaaS platform  318  includes a number of cloud servers, such as one or more cloud servers  320 . The cloud servers  320  are in communication with each other via one or more networks (not shown) and may be in communication with one or more storage devices, for example, the storage appliances described further above in  FIG.  1 A , the onsite storage  312 , or the cloud storage  314 . In various embodiments, the cluster protection system  316  resides in one or more cloud servers, such as the cloud server  320 . In various embodiments, a user interface such as a CSI (described above) enables a user (e.g., a system administrator or a customer) of the SaaS platform  318  to identify a target object in a cluster, or a target cluster among the clusters  306  of the client compute environment  304 , for data backup protection or restoration. 
     In some examples, a backup of the clusters  306  is divided into two phases. In a first phase, various examples generate and store a first (or “resource”) snapshot of a cluster  306  into the offsite cloud storage  314  and, in a second phase, generate and store a second (or “persistent”) snapshot in a PV in the onsite storage  312 . In some examples, one or more second snapshots are generated by the cluster agent  308 . The cluster agent  308  is deployed in the clusters  306  which, in the illustrated example, reside onsite and reside in the same client compute environment  304  as the data management system  310  and onsite storage  312 . The cluster protection system  316  and the cluster agent  308  can communicate directly or through the data management system  310  and onsite storage  312 , for example at  330 . 
     With reference again to  FIG.  3   , in response to a user or system selection of protection for a target object or cluster in the clusters  306 , the cluster agent  308  executes backup operations inside the clusters  306 . At backup operation  322 , the cluster agent  308  receives requests from the cluster protection system  316 . In various examples, the backup operations include collecting metadata from the targeted cluster or object by communicating with cluster APIs and sending the collected metadata to the cluster protection system  316 . The backup operations may include orchestrating execution of pre- and post-scripts in the clusters  306  and, in some examples, spinning off or creating new containers to execute the scripts. The operations may also include orchestrating metadata collection. At backup operation  324 , the cluster agent  308  takes a first (or resource) snapshot and uploads the first snapshot to the cloud storage  314 . The first snapshot includes at least the collected metadata. The first snapshot may include one or more of a group including metadata objects, resources, and configurations. The first snapshot may include one or more of a group including or container-related (e.g., Kubernetes-related) metadata objects, resources, and configurations. The resources may include the resources described further above. In some examples, the cluster agent uploads a compressed version (or tar ball) of the first snapshot to the cloud storage  314 . 
     The backup operations may further include, in operation  326 , taking a second (or persistent) snapshot of the target cluster or the object and storing the second snapshot in a PV in the onsite storage  312 . In some examples, operation  326  is performed by the cluster agent  308 . The second snapshot may include data of the type discussed above, for example application data that might otherwise be lost if a container or pod processing the application data is destroyed or corrupted. The cluster agent may return snapshot “handles” (addresses) of the generated second snapshots to the cluster protection system  316 . The first snapshot and the second snapshots may be included in respective series of first and second snapshots. The first snapshot and the second snapshots may be taken or processed simultaneously or asynchronously. The first snapshot may be taken or processed before the second snapshot is taken or processed, or vice versa. 
     In some examples, the cluster agent  308  orchestrates data movement for a series of second snapshots into the onsite storage  312 . In some examples, data movers can be created or destroyed on demand by the cluster agent  308 . For example, in operation  328 , the cluster agent  308  may copy the second snapshots from the onsite storage  312  to the clusters  306  or download a first snapshot (or compression thereof) from the cloud storage  314  to the clusters  306 . In some examples, the cluster agent  308  orchestrates applying one or more first snapshots and/or one or more second snapshots to the clusters  306  for a restore operation. In some examples, the offsite cloud storage  314  stores first snapshots or first snapshot compressions of the clusters  306 , or a targeted cluster therein, uploaded by the cluster agent  308  upon completion of the snapshots or compressions. 
     In various examples of the cluster protection system  316 , one or more SLAs may be assigned to namespaces and/or clusters. In some examples, the SLAs may also be assigned to other objects such as labels and controllers. Based on the assigned SLAs, the cluster protection system  316  decides when a snapshot is needed for a given object, such as a namespace or a targeted cluster. The cluster protection system  316  can also instruct the cluster agent  308  to perform a restore based on an SLA or a user request, for example in operation  322 . At the time a snapshot is to be taken accordingly, the cluster protection system  316  instructs the cluster agent to trigger a first snapshot for a set or subset of all resources belonging to the targeted cluster or a namespace. The cluster or namespace targeting may be based on which object is to be protected under the applicable SLA or user/machine instruction. 
     With reference to  FIG.  4   , the restore of a protected object or target cluster is now described. In some examples, the backed-up data used in a restore operation flows in the reverse way of the taking of the respective snapshots. When a user triggers a restore operation, for example by initiating a restoration request, the cluster protection system  316  performs restore operations. The restore operations may include identifying which second snapshots (containing application data for example) and which first snapshots (containing metadata, resources, and/or configurations for example) are required to fulfil the restore request. In operation  402 , the cluster protection system  316  instructs the cluster agent  308  to retrieve the identified second snapshot (or snapshots) from the applicable PV in the onsite storage  312 , and to download a corresponding first snapshot (or snapshots) from the cloud storage  314 . The data in the retrieved and downloaded snapshots is used to restore the target object or cluster among the clusters  306 . In various examples, the download of the first snapshot or snapshots from the cloud storage occurs in operation  404 , and the retrieval of the second snapshot or snapshots occurs in operation  406 . In some examples, the retrieved and downloaded data is orchestrated for application to the targeted cluster to form a restore target or object. In various examples, the data orchestration and application operations are performed by the cluster agent  308 . In various examples, the data orchestration and application operations are performed by the cluster protection system  316  or, in some examples, by the cluster agent  308  and cluster protection system  316  acting in concert. A restore target or object could include one or more of a namespace, a PV, or a set of objects under a certain label, and so forth. Combinations of one or more of these aspects are possible. 
     Some embodiments include methods. With reference to  FIG.  5   , example operations in a method  500  of protecting data in a container management framework comprise, at operation  502 , identifying a target cluster or an object in the container management framework; at operation  504 , identifying application data and metadata associated with the target cluster or the object; at operation  506 , generating a first snapshot of the target cluster or the object, the first snapshot including at least the metadata; at operation  508 , storing the first snapshot in offsite cloud storage; at operation  510 , generating a second snapshot of the target cluster, the second snapshot including at least the application data; and, at operation  512 , storing the second snapshot in a persistent volume in onsite storage. 
     In some examples, the first snapshot further comprises resource data. In some examples, a resource to which the resource data relates includes a cluster controller. 
     In some examples, the cluster controller controls a pod of containers. 
     In some examples, the first snapshot further comprises a cluster configuration. 
     In some examples, the first snapshot further comprises a persistent volume claim (PVC). 
     In some examples, the method  500  further comprises receiving a restore request for the target cluster or the object and, based on the received restore request, restoring the target cluster or the object using the first snapshot and the second snapshot. 
     In some examples, a non-transitory machine-readable medium includes instructions which, when read by a machine, cause a machine to perform operations in a method of protecting data in a container management framework, the operations comprising at least those summarized above, or described elsewhere herein. 
       FIG.  6    is a block diagram  600  illustrating an example architecture  606  of software that can be used to implement various embodiments described herein.  FIG.  6    is merely a non-limiting example of a software architecture, and it will be appreciated that many other architectures can be implemented to facilitate the functionality described herein. In various embodiments, the software is implemented by a hardware layer  652 , which includes a processor  654  operating on instructions  604 , a memory  656  storing instructions  604 , and other hardware  658 . For some embodiments, the hardware layer  652  is implemented using a machine  700  of  FIG.  7    that includes processors  710 , memory  730 , and I/O components  750 . In this example architecture  606 , the software architecture  606  can be conceptualized as a stack of layers where each layer may provide a particular functionality. For example, the software architecture  606  includes layers such as an operating system  602 , libraries  620 , frameworks  618 , and applications  616 . Operationally, the applications  616  invoke API calls  608  through the software stack and receive messages  612  in response to the API calls  608 , consistent with some embodiments. 
     In various implementations, the operating system  602  manages hardware resources and provides common services. The operating system  602  includes, for example, a kernel  622 , services  624 , and drivers  626 . The kernel  622  acts as an abstraction layer between the hardware and the other software layers, consistent with some embodiments. For example, the kernel  622  provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionalities. The services  624  can provide other common services for the other software layers. The drivers  626  are responsible for controlling or interfacing with the underlying hardware, according to some embodiments. For instance, the drivers  626  can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth. 
     In some embodiments, the libraries  620  provide a low-level common infrastructure utilized by the applications  616 . The libraries  620  can include system libraries  644  (e.g., C standard library) that can provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries  620  can include API libraries  646  such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries  620  can also include a wide variety of other libraries  648  to provide many other APIs to the applications  616 . 
     The frameworks  618  provide a high-level common infrastructure that can be utilized by the applications  616 , according to some embodiments. For example, the frameworks  618  provide various GUI functions, high-level resource management, high-level location services, and so forth. The frameworks  618  can provide a broad spectrum of other APIs that can be utilized by the applications  616 , some of which may be specific to a particular operating system or platform. 
     In some embodiments, the applications  616  include a built-in application  638  and a broad assortment of other applications such as a third-party application  640 . According to some embodiments, the applications  616  are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications  616 , structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application  640  (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application  640  can invoke the API calls  608  provided by the operating system  602  to facilitate functionality described herein. 
       FIG.  7    illustrates a diagrammatic representation of an example machine  700  in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies of various embodiments described herein. Specifically,  FIG.  7    shows a diagrammatic representation of the machine  700  in the example form of a computer system, within which instructions  716  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  700  to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions  716  may cause the machine  700  to execute one of more of the methods disclosed herein. Additionally, or alternatively, the instructions  716  may implement other methods or processes described with reference to  FIG.  5   , or as described elsewhere herein. The instructions  716  transform the general, non-programmed machine  700  into a particular machine  700  programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine  700  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  700  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine  700  may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  716 , sequentially or otherwise, that specify actions to be taken by the machine  700 . Further, while only a single machine  700  is illustrated, the term “machine” shall also be taken to include a collection of machines  700  that individually or jointly execute the instructions  716  to perform any one or more of the methodologies discussed herein. 
     The machine  700  may include processors  710 , memory  730 , and I/O components  750 , which may be configured to communicate with each other such as via a bus  702 . In some embodiments, the processors  710  (e.g., a CPU, a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a GPU, a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor  712  and a processor  714  that may execute the instructions  716 . The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although  FIG.  7    shows multiple processors  710 , the machine  700  may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof. 
     The memory  730  may include a main memory  732 , a static memory  734 , and a storage unit  736 , both accessible to the processors  710  such as via the bus  702 . The main memory  730 , the static memory  734 , and storage unit  736  store the instructions  716  embodying any one or more of the methodologies or functions described herein. The instructions  716  may also reside, completely or partially, within the main memory  732 , within the static memory  734 , within the storage unit  736 , within at least one of the processors  710  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  700 . The storage unit  736  can comprise a machine readable medium  738  for storing the instructions  716 . 
     The I/O components  750  may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  750  that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components  750  may include many other components that are not shown in  FIG.  7   . The I/O components  750  are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various embodiments, the I/O components  750  may include output components  752  and input components  754 . The output components  752  may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components  754  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. 
     In further embodiments, the I/O components  750  may include biometric components  756 , motion components  758 , environmental components  760 , or position components  762 , among a wide array of other components. For example, the biometric components  756  may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components  758  may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components  760  may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components  762  may include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O components  750  may include communication components  764  operable to couple the machine  700  to a network  780  or devices  770  via a coupling  782  and a coupling  772 , respectively. For example, the communication components  764  may include a network interface component or another suitable device to interface with the network  780 . In further examples, the communication components  764  may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices  770  may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB). 
     Moreover, the communication components  764  may detect identifiers or include components operable to detect identifiers. For example, the communication components  764  may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components  764 , such as location via IP geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth. 
     The various memories (i.e.,  730 ,  732 ,  734 , and/or memory of the processor(s)  710 ) and/or storage unit  736  may store one or more sets of instructions and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions  716 ), when executed by processor(s)  710 , cause various operations to implement the disclosed embodiments. 
     As used herein, the terms “machine-storage medium,” “device-storage medium,” and “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms refer to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions and/or data. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), EEPROM, FPGA, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below. 
     In various embodiments, one or more portions of the network  780  may be an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, a portion of the PSTN, a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network  780  or a portion of the network  780  may include a wireless or cellular network, and the coupling  782  may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling  782  may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long range protocols, or other data transfer technology. 
     The instructions  716  may be transmitted or received over the network  780  using a transmission medium via a network interface device (e.g., a network interface component included in the communication components  764 ) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  716  may be transmitted or received using a transmission medium via the coupling  772  (e.g., a peer-to-peer coupling) to the devices  770 . The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure. The terms “transmission medium” and “signal medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions  716  for execution by the machine  700 , and includes digital or analog communications signals or other intangible media to facilitate communication of such software. Hence, the terms “transmission medium” and “signal medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal. 
     The terms “machine-readable medium,” “computer-readable medium” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. 
     Other embodiments can comprise corresponding systems, apparatus, and computer programs recorded on one or more machine readable media, each configured to perform the operations of the methods. 
     The disclosed technology may be described in the context of computer-executable instructions, such as software or program modules, being executed by a computer or processor. The computer-executable instructions may comprise portions of computer program code, routines, programs, objects, software components, data structures, or other types of computer-related structures that may be used to perform processes using a computer. In some cases, hardware or combinations of hardware and software may be substituted for software or used in place of software. 
     Computer program code used for implementing various operations or aspects of the disclosed technology may be developed using one or more programming languages, including an object-oriented programming language such as Java or C++, a procedural programming language such as the “C” programming language or Visual Basic, or a dynamic programming language such as Python or JavaScript. In some cases, computer program code or machine-level instructions derived from the computer program code may execute entirely on an end user&#39;s computer, partly on an end user&#39;s computer, partly on an end user&#39;s computer and partly on a remote computer, or entirely on a remote computer or server. 
     For purposes of this document, it should be noted that the dimensions of the various features depicted in the Figures may not necessarily be drawn to scale. 
     For purposes of this document, reference in the specification to “an embodiment,” “one embodiment,” “some embodiments,” or “another embodiment” may be used to describe different embodiments and do not necessarily refer to the same embodiment. 
     For purposes of this document, a connection may be a direct connection or an indirect connection (e.g., via another part). In some cases, when an element is referred to as being connected or coupled to another element, the element may be directly connected to the other element or indirectly connected to the other element via intervening elements. When an element is referred to as being directly connected to another element, then there are no intervening elements between the element and the other element. 
     For purposes of this document, the term “based on” may be read as “based at least in part on.” 
     For purposes of this document, without additional context, use of numerical terms such as a “first” object, a “second” object, and a “third” object may not imply an ordering of objects, but may instead be used for identification purposes to identify different objects. 
     For purposes of this document, the term “set” of objects may refer to a “set” of one or more of the objects. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.