Patent Publication Number: US-2021173852-A1

Title: System and method for hyperconvergence at the datacenter

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is related to and claims priority under 35 U.S. § 119(e) from Indian Provisional Application No. 201941050392, filed Dec. 6, 2019, titled “SYSTEM AND METHOD FOR HYPERCONVERGENCE AT THE DATACENTER,” the entire contents of which are incorporated herein by reference for all purposes. 
     BACKGROUND 
     The following description is provided to assist the understanding of the reader. None of the information provided or references cited is admitted to be prior art. 
     Virtual computing systems are widely used in a variety of applications. Virtual computing systems include one or more host machines running one or more virtual machines concurrently. The virtual machines utilize the hardware resources of the underlying host machines. Each virtual machine may be configured to run an instance of an operating system. Modern virtual computing systems allow several operating systems and several software applications to be safely run at the same time on the virtual machines of a single host machine, thereby increasing resource utilization and performance efficiency. However, the present-day virtual computing systems have limitations due to their configuration and the way they operate. 
     SUMMARY 
     Aspects of the present disclosure relate generally to a virtualization environment, and more particularly to a system and method for multi-cluster storage expansion. 
     An illustrative embodiment disclosed herein is an apparatus including a processor with programmed instructions to receive a request to process storage data using a compute application, determine that a first location of the storage data is in a first storage resource in a first cluster of compute, storage, and network resources, determine that a second location of the compute application is in a first compute resource of a second cluster of compute, storage, and network resources, determine whether the first cluster includes a second compute resource that is compatible with the compute application, and respectively, either, based on a first compatibility determination, migrate the compute application from the first compute resource to the second compute resource, or, based on a second compatibility determination, replicate the storage data from the first storage resource to a second storage resource in the second cluster. 
     In some embodiments, the first compatibility determination includes that the second compute resource is compatible with the compute application. In some embodiments, the second compatibility determination includes that the second compute resource is not compatible with the compute application. In some embodiments, one of the first and second cluster is a private cluster, and the other of the first and second cluster is a public cluster. In some embodiments, the first location is exposed through a first API and the second location is exposed through a second API. In some embodiments, the first and second location are part of a single namespace. In some embodiments, the first storage resource and the second storage resource belong to a distributed object store. 
     Another illustrative embodiment disclosed herein is a non-transitory computer readable storage medium having instructions stored thereon that, upon execution by a processor, causes the processor to perform operations including receiving a request to process storage data using a compute application, determining that a first location of the storage data is in a first storage resource in a first cluster of compute, storage, and network resources, determining that a second location of the compute application is in a first compute resource of a second cluster of compute, storage, and network resources, determining whether the first cluster includes a second compute resource that is compatible with the compute application, and respectively, either, based on a first compatibility determination, migrating the compute application from the first compute resource to the second compute resource, or, based on a second compatibility determination, replicating the storage data from the first storage resource to a second storage resource in the second cluster. 
     Another illustrative embodiment disclosed herein is a computer-implemented method including receiving, by a processor, a request to process storage data using a compute application, determining, by the processor, that a first location of the storage data is in a first storage resource in a first cluster of compute, storage, and network resources, determining, by the processor, that a second location of the compute application is in a first compute resource of a second cluster of compute, storage, and network resources, determining, by the processor, whether the first cluster includes a second compute resource that is compatible with the compute application, and respectively, either, based on a first compatibility determination, migrating, by the processor, the compute application from the first compute resource to the second compute resource, or, based on a second compatibility determination, replicating, by the processor, the storage data from the first storage resource to a second storage resource in the second cluster. 
     Further details of aspects, objects, and advantages of the invention are described below in the detailed description, drawings, and claims. Both the foregoing general description and the following detailed description are exemplary and explanatory, and are not intended to be limiting as to the scope of the invention. Particular embodiments may include all, some, or none of the components, elements, features, functions, operations, or steps of the embodiments disclosed above. The subject matter which can be claimed comprises not only the combinations of features as set out in the attached claims but also any other combination of features in the claims, wherein each feature mentioned in the claims can be combined with any other feature or combination of other features in the claims. Furthermore, any of the embodiments and features described or depicted herein can be claimed in a separate claim and/or in any combination with any embodiment or feature described or depicted herein or with any of the features of the attached claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is an example block diagram of a virtual computing system, in accordance with some embodiments of the present disclosure. 
         FIG. 2  is an example block diagram of a multi-cluster environment, in accordance with some embodiments of the present disclosure. 
         FIG. 3  is a flowchart of an example method for implementing hyperconvergence at the data center, in accordance with some embodiments of the present disclosure. 
     
    
    
     The foregoing and other features of the present disclosure will become apparent from the following description and appended claims, taken in conjunction with the accompanying drawings. Understanding that these drawings depict only several embodiments in accordance with the disclosure and are, therefore, not to be considered limiting of its scope, the disclosure will be described with additional specificity and detail through use of the accompanying drawings. 
     DETAILED DESCRIPTION 
     In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, and designed in a wide variety of different configurations, all of which are explicitly contemplated and make part of this disclosure. 
     Enterprises today creates silos across geographies and clusters. Because compute and storage resources are spread out, this approach is wasteful, avoids richer analytics and data processing, and results in lower data throughput, higher latency, and more network hops. This approach is not flexible or adaptive enough to adequately handle the dynamic nature of storage and compute resources that may be created or moved on the fly from one network location to the other. Moreover, this approach does not scale well, especially with computationally intensive or data intensive workloads. Other enterprises force customers to move data to public clouds. This can be prohibitively expensive for customers. What is needed is an approach that leverages data locality to improve efficient use of compute, storage, and network resources without exceeding reasonable cost. 
     Some embodiments of the present disclosure include an infrastructure which provides the single namespace and locality for application convergence. The single namespace is across a federation of public and private clusters. The single namespace allows intelligent access of data location information anywhere. The data location information can be accessed through an API. The data location information can be used to provide read locality such as smart caching, write locality, storage locality such as local buckets for object storage, and local compute placement. Locality offers better efficiency of resource usage, allows for compute, storage, and network convergence at the multi-cluster or datacenter level, and results in better performance and cost savings. 
     Virtualization Technology and Environment 
     Referring now to  FIG. 1 , a virtual computing system  100  is shown, in accordance with some embodiments of the present disclosure. The virtual computing system  100  includes a plurality of nodes, such as a first node  105 A, a second node  105 B, and a third node  105 C. The nodes may be collectively referred to herein as “nodes  105 .” Each of the nodes  105  may also be referred to as a “host” or “host machine.” The first node  105 A includes an object virtual machine (“OVMs”)  111 A and  111 B (collectively referred to herein as “OVMs  111 ”), a controller virtual machine (“CVM”)  115 A, and a hypervisor  125 A. Similarly, the second node  105 B includes OVMs  112 A and  112 B (collectively referred to herein as “OVMs  112 ”), a CVM  115 B, and a hypervisor  125 B, and the third node  105 C includes OVMs  113 A and  113 B (collectively referred to herein as “OVMs  113 ”), a CVM  115 C, and a hypervisor  125 C. The OVMs  111 ,  112 , and  113  may be collectively referred to herein as “OVMs  110 .” The CVMs  115 A,  115 B, and  115 C may be collectively referred to herein as “CVMs  115 .” The nodes  105  are connected to a network  165 . 
     The virtual computing system  100  also includes a storage pool  140 . The storage pool  140  may include network-attached storage (NAS)  150  and direct-attached storage (DAS)  145 A,  145 B, and  145 C (collectively referred to herein as DAS  145 ). The NAS  150  is accessible via the network  165  and, in some embodiments, may include cloud storage  155 , as well as local area network (“LAN”) storage  160 . In contrast to the NAS  150 , which is accessible via the network  165 , each of the DAS  145 A, the DAS  145 B, and the DAS  145 C includes storage components that are provided internally within the first node  105 A, the second node  105 B, and the third node  105 C, respectively, such that each of the first, second, and third nodes may access its respective DAS without having to access the network  165 . 
     The CVM  115 A may include one or more virtual disks (“vdisks”)  120 A, the CVM  115 B may include one or more vdisks  120 B, and the CVM  115 C may include one or more vdisks  120 C. The vdisks  120 A, the vdisks  120 B, and the vdisks  120 C are collectively referred to herein as “vdisks  120 .” The vdisks  120  may be a logical representation of storage space allocated from the storage pool  140 . Each of the vdisks  120  may be located in a memory of a respective one of the CVMs  115 . The memory of each of the CVMs  115  may be a virtualized instance of underlying hardware, such as the RAMs  135  and/or the storage pool  140 . The virtualization of the underlying hardware is described below. 
     In some embodiments, the CVMs  115  may be configured to run a distributed operating system in that each of the CVMs  115  run a subset of the distributed operating system. In some embodiments, the CVMs  115 , and the underlying storage of the nodes (e.g., nodes  105 ) on which the CVMs  115  are running, form one or more storage clusters (e.g., Nutanix Operating System (“NOS”) clusters). In some embodiments, the one or more NOS clusters include greater than or fewer than the CVMs  115 . In some embodiments, each of the CVMs  115  run a separate, independent instance of an operating system. In some embodiments, the one or more NOS clusters may be referred to as a storage layer. For sake of brevity, the storage clusters (e.g., NOS clusters) are herein referred to as clusters. 
     In some embodiments, the OVMs  110  form an OVM cluster. OVMs of an OVM cluster may be configured to share resources with each other. The OVMs in the OVM cluster may be configured to access storage from the NOS cluster using one or more of the vdisks  120  as a storage unit. The OVMs in the OVM cluster may be configured to run software-defined object storage service, such as Nutanix Buckets™. The OVM cluster may be configured to create buckets, add objects to the buckets, and manage the buckets and objects. In some embodiments, the OVM cluster include greater than or fewer than the OVMs  110 . 
     Multiple OVM clusters and/or multiple NOS clusters may exist within a given virtual computing system (e.g., the virtual computing system  100 ). The one or more OVM clusters may be referred to as a client layer or object layer. The OVM clusters may be configured to access storage from multiple NOS clusters. Each of the OVM clusters may be configured to access storage from a same NOS cluster. A central management system, such as Prism Central, may manage a configuration of the multiple OVM clusters and/or multiple NOS clusters. The configuration may include a list of OVM clusters, a mapping of each OVM cluster to a list of NOS clusters from which the OVM cluster may access storage, and/or a mapping of each OVM cluster to a list of vdisks that the OVM cluster owns or has access to. 
     Each of the OVMs  110  and the CVMs  115  is a software-based implementation of a computing machine in the virtual computing system  100 . The OVMs  110  and the CVMs  115  emulate the functionality of a physical computer. Specifically, the hardware resources, such as CPU, memory, storage, etc., of a single physical server computer (e.g., the first node  105 A, the second node  105 B, or the third node  105 C) are virtualized or transformed by the respective hypervisor (e.g. the hypervisor  125 A, the hypervisor  125 B, and the hypervisor  125 C), into the underlying support for each of the OVMs  110  and the CVMs  115  that may run its own operating system, a distributed operating system, and/or applications on the underlying physical resources just like a real computer. By encapsulating an entire machine, including CPU, memory, operating system, storage devices, and network devices, the OVMs  110  and the CVMs  115  are compatible with most standard operating systems (e.g. Windows, Linux, etc.), applications, and device drivers. Thus, each of the hypervisors  125  is a virtual machine monitor that allows the single physical server computer to run multiple instances of the OVMs  110  (e.g. the OVM  111 ) and at least one instance of a CVM  115  (e.g. the CVM  115 A), with each of the OVM instances and the CVM instance sharing the resources of that one physical server computer, potentially across multiple environments. By running the multiple instances of the OVMs  110  on a node of the nodes  105 , multiple workloads and multiple operating systems may be run on the single piece of underlying hardware computer to increase resource utilization and manage workflow. 
     The hypervisors  125  of the respective nodes  105  may be configured to run virtualization software, such as, ESXi from VMWare, AHV from Nutanix, Inc., XenServer from Citrix Systems, Inc., etc. The virtualization software on the hypervisors  125  may be configured for managing the interactions between the respective OVMs  110  (and/or the CVMs  115 ) and the underlying hardware of the respective nodes  105 . Each of the CVMs  115  and the hypervisors  125  may be configured as suitable for use within the virtual computing system  100 . 
     In some embodiments, each of the nodes  105  may be a hardware device, such as a server. For example, in some embodiments, one or more of the nodes  105  may be an NX-1000 server, NX-3000 server, NX-5000 server, NX-6000 server, NX-8000 server, etc. provided by Nutanix, Inc. or server computers from Dell, Inc., Lenovo Group Ltd. or Lenovo PC International, Cisco Systems, Inc., etc. In other embodiments, one or more of the nodes  105  may be another type of hardware device, such as a personal computer, an input/output or peripheral unit such as a printer, or any type of device that is suitable for use as a node within the virtual computing system  100 . In some embodiments, the virtual computing system  100  may be part of a data center. 
     The first node  105 A may include one or more central processing units (“CPUs”)  130 A, the second node  105 B may include one or more CPUs  130 B, and the third node  105 C may include one or more CPUs  130 C. The CPUs  130 A,  130 B, and  130 C are collectively referred to herein as the CPUs  130 . The CPUs  130  may be configured to execute instructions. The instructions may be carried out by a special purpose computer, logic circuits, or hardware circuits of the first node  105 A, the second node  105 B, and the third node  105 C. The CPUs  130  may be implemented in hardware, firmware, software, or any combination thereof. The term “execution” is, for example, the process of running an application or the carrying out of the operation called for by an instruction. The instructions may be written using one or more programming language, scripting language, assembly language, etc. The CPUs  130 , thus, execute an instruction, meaning that they perform the operations called for by that instruction. 
     The first node  105 A may include one or more random access memory units (“RAM”)  135 A, the second node  105 B may include one or more RAM  135 B, and the third node  105 C may include one or more RAM  135 C. The RAMs  135 A,  135 B, and  135 C are collectively referred to herein as the RAMs  135 . The CPUs  130  may be operably coupled to the respective one of the RAMs  135 , the storage pool  140 , as well as with other elements of the respective ones of the nodes  105  to receive, send, and process information, and to control the operations of the respective underlying node. Each of the CPUs  130  may retrieve a set of instructions from the storage pool  140 , such as, from a permanent memory device like a read only memory (“ROM”) device and copy the instructions in an executable form to a temporary memory device that is generally some form of random access memory (“RAM”), such as a respective one of the RAMs  135 . One of or both of the ROM and RAM be part of the storage pool  140 , or in some embodiments, may be separately provisioned from the storage pool. The RAM may be stand-alone hardware such as RAM chips or modules. Further, each of the CPUs  130  may include a single stand-alone CPU, or a plurality of CPUs that use the same or different processing technology. 
     Each of the DAS  145  may include a variety of types of memory devices. For example, in some embodiments, one or more of the DAS  145  may include, but is not limited to, any type of RAM, ROM, flash memory, magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips, etc.), optical disks (e.g., compact disk (“CD”), digital versatile disk (“DVD”), etc.), smart cards, solid state devices, etc. Likewise, the NAS  150  may include any of a variety of network accessible storage (e.g., the cloud storage  155 , the LAN storage  160 , etc.) that is suitable for use within the virtual computing system  100  and accessible via the network  165 . The storage pool  140 , including the NAS  150  and the DAS  145 , together form a distributed storage system configured to be accessed by each of the nodes  105  via the network  165 , one or more of the OVMs  110 , one or more of the CVMs  115 , and/or one or more of the hypervisors  125 . 
     Each of the nodes  105  may be configured to communicate and share resources with each other via the network  165 , including the respective one of the CPUs  130 , the respective one of the RAMs  135 , and the respective one of the DAS  145 . For example, in some embodiments, the nodes  105  may communicate and share resources with each other via one or more of the OVMs  110 , one or more of the CVMs  115 , and/or one or more of the hypervisors  125 . One or more of the nodes  105  may be organized in a variety of network topologies. 
     The network  165  may include any of a variety of wired or wireless network channels that may be suitable for use within the virtual computing system  100 . For example, in some embodiments, the network  165  may include wired connections, such as an Ethernet connection, one or more twisted pair wires, coaxial cables, fiber optic cables, etc. In other embodiments, the network  165  may include wireless connections, such as microwaves, infrared waves, radio waves, spread spectrum technologies, satellites, etc. The network  165  may also be configured to communicate with another device using cellular networks, local area networks, wide area networks, the Internet, etc. In some embodiments, the network  165  may include a combination of wired and wireless communications. 
     Although three of the plurality of nodes (e.g., the first node  105 A, the second node  105 B, and the third node  105 C) are shown in the virtual computing system  100 , in other embodiments, greater than or fewer than three nodes may be used. Likewise, although only two of the OVMs are shown on each of the first node  105 A (e.g. the OVMs  111 ), the second node  105 B, and the third node  105 C, in other embodiments, greater than or fewer than two OVMs may reside on some or all of the nodes  105 . 
     Objects are collections of unstructured data that includes the object data and object metadata describing the object or the object data. A bucket is a logical construct exposed to users that contains the objects. A deployment can have multiple buckets. The buckets are backed by vdisks, which are backed by an underlying storage, such as a storage pool, a NOS container, a SAN, or a NAS. In some embodiments, a vdisk appears to the bucket, a CVM, or other VM, as a SAN or NAS storage. The bucket/CVM reads or writes to the vdisk using, for example, SCSI or NFS protocol. In some embodiments, the vdisk encapsulates the SCSI commands in iSCSI and sends the iSCSI commands across the network to the SAN hardware that is backing the vdisk. In some embodiments, the vdisk encapsulates and/or forwards the NFS commands across the network to the NAS storage that is backing the vdisk. 
     Object storage service (OSS), is a layered service being built over NOS. OSS uses the power of the NOS offering and builds an efficient and scalable object store service on top. Users (e.g., clients, client devices or client applications) read and write objects to the OSS and use GET and PUT calls for read and write operations. In some embodiments, an entire object is written and partial writes, appends or overwrites are not permitted. For reads and writes, data flows through OSS components before being stored in NOS storage. The OSS may run on one or more OVMs, such as the OVMs  110 . The OSS is herein referred to as the object layer. 
     Regions from vdisks, such as the vdisks  120 , are allocated to buckets. A region provides the high-level mapping between at least one of an object or a bucket and the corresponding locations on a vdisk (e.g., a vDisk ID, vdisk offset, and vdisk length). Vdisks may be subdivided (e.g. chunked) into multiple fixed-size regions. A region can include portions (e.g., portions, chunks, blocks, locations) of multiple vdisks. 
     It is to be understood again that only certain components and features of the virtual computing system  100  are shown and described herein. Nevertheless, other components and features that may be needed or desired to perform the functions described herein are contemplated and considered within the scope of the present disclosure. It is also to be understood that the configuration of the various components of the virtual computing system  100  described above is only an example and is not intended to be limiting in any way. Rather, the configuration of those components may vary to perform the functions described herein. 
     Hyperconvergence at the Datacenter 
     Referring now to  FIG. 2 , a multi-cluster environment  200  is shown, in accordance with some embodiments of the present disclosure. The multi-cluster environment  200  includes a plurality of clusters, such as clusters  205 A and  205 B (collectively, referred to as “clusters  205 ”), that are coupled to each other. Each of the clusters includes a plurality of compute resources and storage resources. For example, the cluster  205 A includes compute resources  210 A- 210 L and storage resources  215 A- 215 K, and the cluster  205 B includes compute resources  210 M- 210 Z and storage resources  215 M- 215 Y. The compute resources are collectively referred to as “compute resources  210 ” and the storage resources are collectively referred to as “storage resources  215 .” Compute resources include processors such as CPU  130 A with respect to  FIG. 1 , RAM, etc. Storage resources include physical disks, virtual disks, buckets, any of the components of the storage pool  140  with respect to  FIG. 1 , etc. The multi-cluster environment  200  includes a resource manager  220  that is coupled to each of the clusters  205 . The multi-cluster environment  200  includes a client  225  that is coupled to the resource manager  220 . 
     In some embodiments, at least one of the clusters  205  is a private cluster. In some embodiments, at least one of the clusters  205  is a public cluster (e.g., a public cloud). In some embodiments, the clusters  205  are in a same datacenter (e.g., site). In some embodiments, the cluster  205 A is in a first datacenter and the cluster  205 B is in a second datacenter. In some embodiments, each of the clusters includes a virtual computing system  100 , or some components form the virtual computing system  100 . In some embodiments, the multi-cluster environment  200  (e.g., the clusters  205  in the multi-cluster environment  200 ) includes greater than two clusters. 
     The resource manager  220  includes a processor having programed instructions (herein, the resource manager  220  includes programmed instructions) to manage (e.g., move, migrate, replicate, copy, clone, monitor, send out alerts for, etc.) resources (e.g., the compute resources  210 , the storage resources  215 , network resources, etc.). The resource manager  220  includes programmed instructions to receive resource requests. The resource requests may be from the client  205  (e.g., a user, an application, a cluster, a node, a virtual machine, etc.). The resource request may be a request to process (e.g., compute, calculate, determine, run, etc.) storage data (e.g., object storage data, file system data, database data, containerized data, etc.) using a compute application (e.g., a program having computing capabilities, an application, an app, a desktop application, a web browser, a mobile app, an operating system, etc.). In some embodiments, the compute application is a set of instructions stored on a (e.g., non-transitory) computer readable medium that is processed by a processor such as the CPU  130 A with respect to  FIG. 1 . The resource request may be done using an application programming interface (API). 
     The resource manager  220  includes programmed instructions to determine a cluster location for the storage data and the compute application. For example, the resource manager  220  includes programmed instructions to determine that the storage data is located in a first storage resource of a first cluster and the compute application is located in (e.g., in, coupled to, associated with, run by, processed by) a first compute resource of a second cluster. The locations, identifiers, and other information of the storage data and the compute application are exposed through APIs, such as representational state transfer (REST) APIs or remote procedure commands (RPCs). For example, the resource manager  220  includes programmed instructions to send a request (e.g., an API request, an API call) to an endpoint (e.g., the cluster  205 A, a CVM on the cluster  205 A such as the CVM  115 A with respect to  FIG. 1 , etc.) to determine data and applications located in that cluster, and at which resources those data and applications are located. In some embodiments, the endpoint sends a response (e.g., responds, returns a response, etc.) to the request. In some embodiments, a first set of APIs is used to determine a location or other information of storage data, and a second set of APIs to determine location or other information of a compute application. 
     The resource manager  220  includes programmed instructions to determine whether the first cluster includes a second compute resource (e.g., a second compute resource located on the same cluster as the storage data) that is compatible with the compute application. If the resource manager determines that the second compute resource is compatible with the compute application, the resource manager  220  includes programmed instructions to migrate the compute application from the first compute resource to the second compute resource. If the resource manager determines that the second compute resource is not compatible with the compute application, the resource manager  220  includes programmed instructions to replicate the storage data from the first storage resource to a second storage resource in the second cluster. 
     In some embodiments, any clusters and their corresponding locations (e.g., the first and second location) and identifiers are part of a single (e.g., global) namespace. The knowledge of multiple OVM clusters and their configurations may be stored in the resource manager  220 , a multi-cluster manager interfacing with the clusters, or some other component of a virtualization environment such as the virtual computing system  100  or the multi-cluster environment  200 . For example, each cluster is given a cluster federation identifier (ID) and a resource manager  220  instance to join. Each cluster can keep a watch on the membership change of the federation. A single cluster can join multiple federations. On the resource manager  220 , there can be three levels of information: federation membership information/ID, cluster information/ID, and bucket name/ID. 
     Each of the elements or entities of the virtual computing system  100  and the multi-cluster environment  200  (e.g., clusters  205 , compute resources  210 , storage resources  215 , resource manager  220 ), is implemented using hardware, software, or a combination of hardware or software, in one or more embodiments. The elements or entities of the virtual computing system  100  and the multi-cluster environment  200  can include any application, program, library, script, task, service, process or any type and form of executable instructions executed by one or more processors (e.g., the CPU  130 A), in one or more embodiments. Each of the one or more processors is hardware. The instructions may be stored on one or more computer readable and/or executable storage media including non-transitory storage media such as non-transitory storage media in the storage pool  140  with respect to  FIG. 1 . 
     Referring now to  FIG. 3 , a flowchart of an example method  300  for implementing hyperconvergence at the data center is illustrated. The method  300  may be implemented using, or performed by, a resource manager  220  or processor associated with the resource manager  220 , which is detailed herein with respect to the virtual computing system  100  and the multi-cluster environment  200 . Additional, fewer, or different operations may be performed in the method  300  depending on the embodiment. 
     A processor, such as the CPU  130 A, associated with a resource manager, such as the resource manager  220 , receives a request to process storage data using a compute application ( 302 ). The processor determines that a first location of the storage data is in a first storage resource in a first cluster of compute, storage, and network resources ( 304 ). In some embodiments, the first cluster is one of a private cluster and a public cluster. In some embodiments, the first storage resource belongs to a distributed object store. 
     The processor determines that a second location of the compute application is in a first compute resource of a second cluster of compute, storage, and network resources ( 306 ). In some embodiments, the second cluster is the other of a private cluster and a public cluster. In some embodiments, the first location is exposed through a first API and the second location is exposed through a second API. In some embodiments, the first and second location are part of a single namespace. 
     The processor determines whether the first cluster includes a second compute resource that is compatible with the compute application ( 308 ). The processor, respectively, either migrates the compute application from the first compute resource to the second compute resource based on a first compatibility determination, or replicates the storage data from the first storage resource to a second storage resource in the second cluster based on a second compatibility determination ( 310 ). In some embodiments, the first compatibility determination includes that the second compute resource is compatible with the compute application. In some embodiments, the second compatibility determination includes that the second compute resource is not compatible with the compute application. In some embodiments, the second storage resource belongs to the distributed object store. 
     It is to be understood that any examples used herein are simply for purposes of explanation and are not intended to be limiting in any way. 
     The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being “operably couplable,” to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components. 
     With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity. 
     It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.” Further, unless otherwise noted, the use of the words “approximate,” “about,” “around,” “substantially,” etc., mean plus or minus ten percent. 
     The foregoing description of illustrative embodiments has been presented for purposes of illustration and of description. It is not intended to be exhaustive or limiting with respect to the precise form disclosed, and modifications and variations are possible in light of the above teachings or may be acquired from practice of the disclosed embodiments. It is intended that the scope of the invention be defined by the claims appended hereto and their equivalents.