Shared Enterprise Cloud

Abstract

Embodiments of the present disclosure relate to a shared enterprise cloud. A cloud enterprise resource management system enables one or more clients of resource pooling and sharing services to share computing resources belonging to different data centers. Each data center includes a first partition of computing resources and a second partition of computing resources. The first partition is designated as being reserved for use by the enterprise operating the data center. The second partition is designated as being available for use by one or more clients of resource pooling and sharing services. A workload manager in each data center predicts a workload and (i) transfers a first computing resource from the first partition to the second partition, wherein when the predicted workload is below a first threshold, and (ii) when the predicted workload is above a second threshold, transfers a second computing resource from the second partition to the first partition.

Description

Technical Field

[0001] The present disclosure generally relates to enterprise and management of cloud computing resources. Background Art

[0002] A data center is a dedicated space for housing computer systems and associated components such as telecommunications and storage systems. Enterprise computing is the use of technology, information systems, and computers within an organization or enterprise. An enterprise server is a computer server that includes programs that collectively serve the needs of the enterprise rather than those required by a single user, unit, or specific application. Cloud computing is the delivery of different services or computing resources over the Internet, including data storage, servers, databases, networking, and software. Cloud-based storage makes it possible to save files to remote databases and retrieve them on demand. A cloud server is a virtual server (rather than a physical server) running in a cloud computing environment. It is built, hosted, and delivered over the Internet via a cloud computing platform and can be accessed remotely. They are also called virtual servers.

[0003] Many enterprises have spare capacity in their data centers. Some large enterprises provide cloud services by utilizing spare computing or storage capacity. However, providing cloud services from spare capacity is not feasible on a small scale because spare server capacity may vary over time. In addition, enterprises may have security issues because they allow their servers to be used by others. Summary of the invention

[0004] Some embodiments of the present disclosure provide a cloud enterprise resource management system that enables one or more clients of resource pooling and sharing services to share computing resources belonging to different data centers. Each data center includes a first partition of computing resources and a second partition of computing resources. The first partition is designated as being reserved for use by the enterprise operating the data center. The second partition is designated as being available for use by one or more clients of resource pooling and sharing services. A workload manager in the data center predicts workload and (i) transfers a first computing resource from the first partition to the second partition, wherein when the predicted workload is below a first threshold, and (ii) when the predicted workload is above a second threshold, transfers a second computing resource from the second partition to the first partition.

[0005] By predicting the workload in the enterprise data center, the computing device acting as the workload manager of the enterprise data center can participate in resource pooling and sharing services and enable the cloud-enterprise resource management system. Based on the predicted workload, the computing device dynamically determines whether to provide spare resources to the resource pooling and sharing services or request to release resources from the resource pooling and sharing services. The resource utilization efficiency of the enterprise data center is improved.

[0006] In some embodiments, a first partition of computing resources (the enterprise partition) is designated as reserved by the enterprise, while a second partition of computing resources (the spare partition) is designated as available for use by one or more clients of a resource pooling and sharing service that coordinates one or more clients to share computing resources belonging to one or more enterprises. In some embodiments, access to the first partition is controlled by a first firewall that allows access by the enterprise and denies access by clients of the resource pooling and sharing service or any entity external to the enterprise.

[0007] In some embodiments, a predicted workload that is sufficiently below the capacity of the enterprise partition (e.g., below a first threshold) is used as an indication that excess computing capacity exists in the enterprise partition, and one or more computing resources can be moved from the enterprise partition to a spare partition.

[0008] In some embodiments, the workload manager instructs the robot to physically disconnect the first computing resource from the first partition and physically connect the first computing resource to the second partition. The workload manager may also communicate with a cloud coordinator of resource pooling and sharing services to provide an identifier of the first computing resource and specify a time frame in which the first computing resource becomes available for use by a client of the resource pooling and sharing services. The cloud coordinator may further support the client of the resource pooling and sharing services to access the first computing resource by using the provided identifier of the first computing resource at the specified time frame. The cloud coordinator also credits the enterprise's account and debits the client's account based on the use of the first computing resource.

[0009] In some embodiments, a predicted workload that is within the failure tolerance of the capacity of the enterprise partition or exceeds the capacity of the enterprise partition (e.g., above a second threshold) is used as an indication that there is insufficient computing capacity in the enterprise partition so that one or more computing resources are moved from the standby partition back to the enterprise partition.

[0010] In some embodiments, the workload manager instructs the robot to physically disconnect the second computing resource from the second partition and physically connect the second computing resource to the first partition. The workload manager may also communicate with the cloud coordinator of the resource pooling and sharing service to request the return or release of the computing resource and receive a reply including an identifier of the computing resource being returned or released. The identifier may identify a computing resource in the second partition that is not used by any client of the resource pooling and sharing service.

[0011] The foregoing summary is intended to serve as a brief introduction to some embodiments of the present disclosure. It is not meant to be an introduction or overview of all inventive subject matter disclosed herein. The following detailed description and the drawings referenced in the detailed description will further describe the embodiments described in the summary as well as other embodiments. Therefore, an overview, detailed description, and drawings are provided in order to understand all embodiments described by this document. In addition, the claimed subject matter is not limited by the illustrative details in the summary, the specific embodiments, and the drawings, but is defined by the appended claims, because the claimed subject matter may be embodied in other specific forms without departing from the spirit of the subject matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings are illustrative embodiments. They do not show all embodiments. In addition or alternatively, other embodiments may be used. Details that may be obvious or unnecessary may be omitted to save space or for more effective description. Some embodiments may be implemented with additional components or steps and / or without all components or steps shown. When the same number appears in different drawings, it refers to the same or similar components or steps.

[0013] Figure 1 A cloud enterprise resource management system supported by resource consolidation and sharing services is illustrated consistent with the illustrative embodiments.

[0014] Figure 2 A data center of an enterprise supporting resource consolidation and shared services is conceptually illustrated consistent with an exemplary embodiment.

[0015] Figure 3a A sequence of operations for migrating computing resources from an enterprise partition to a standby partition is shown consistent with an exemplary embodiment.

[0016] Figure 3b An example sequence of operations for migrating computing resources from a standby partition back to an enterprise partition is shown, consistent with an illustrative embodiment.

[0017] Figure 4a A block diagram of an enterprise workload manager consistent with the illustrative embodiments is conceptually shown.

[0018] Figure 4b A block diagram of a cloud orchestrator consistent with illustrative embodiments is conceptually shown.

[0019] Figure 5 A process for controlling a data center participating in a resource pooling and sharing service according to an exemplary embodiment is conceptually illustrated.

[0020] Figure 6 A block diagram showing components of a data processing system according to an illustrative embodiment of the present disclosure is shown.

[0021] Figure 7 An example cloud computing environment is shown.

[0022] Figure 8 A set of functional abstraction layers provided by a cloud computing environment according to an exemplary embodiment is shown. DETAILED DESCRIPTION

[0023] In the following detailed description, numerous specific details are set forth by way of example in order to provide a thorough understanding of the relevant teachings. However, it should be understood that the present teachings can be practiced without these details. In other instances, well-known methods, procedures, components and / or circuits have been described at a relatively high level without details in order to avoid unnecessarily obscuring aspects of the present teachings.

[0024] Some embodiments of the present disclosure provide resource pooling and sharing services that support a cloud enterprise resource management system. The system allows multiple enterprises to be in a shared cloud service by pooling the extra server capacity of multiple enterprises. In the data center of each participating enterprise, computing resources are divided into enterprise partitions (as enterprise silos) and backup partitions (as backup silos). The computing resources in the enterprise partitions are resources reserved for the use of the enterprise itself, while the computing resources in the backup partitions can be used as cloud servers.

[0025] In some embodiments, in each enterprise data center, the enterprise workload manager is implemented to allocate or assign computing resources to an enterprise partition or a standby partition. The enterprise workload manager solves security issues by maintaining an air gap between the enterprise partition and the standby partition so that external clients of the standby server cannot access the enterprise server. The enterprise workload manager also makes resource allocation decisions between the enterprise partition and the standby partition based on the prediction of the workload required by the enterprise (moving the boundary between the two partitions). At the cloud, the cloud coordinator acts as an agent for all standby servers provided by all participating enterprises. The cloud coordinator matches the request for server capacity with the available standby server capacity in the participating enterprises. In some embodiments, the cloud coordinator also manages a credit system that tracks the amount of billing to users of the standby server and the amount of credit issued to the enterprise providing the standby server.

[0026] For some embodiments, Figure 1 A cloud enterprise resource management system 100 enabled by a resource pooling and sharing service is shown. In the cloud enterprise resource management system 100, enterprises A, B, and C and clients W, X, Y, and Z participate in resource pooling and sharing services, so that idle computing resources of enterprises A, B, and C can be shared with clients W, X, Y, and Z on the cloud.

[0027] To ensure security, each participating enterprise of the resource pooling and sharing service divides its computing resources or servers into an enterprise partition and a spare partition. The computing resources or servers of the enterprise partition are designated as reserved for use by the enterprise, while the computing resources or servers of the spare partition are designated as available for use by the resource pooling and sharing service. Each participating enterprise can move computing resources from its enterprise partition to its spare partition, and vice versa, based on the predicted enterprise workload.

[0028] The resource pooling and sharing services of the cloud enterprise resource management system 100 are implemented by the cloud coordinator 110 and the enterprise workload managers at the participating enterprises. As shown, enterprise A implements the enterprise workload manager 112, enterprise B implements the enterprise workload manager 114, and enterprise C implements the enterprise workload manager 116. The enterprise workload managers of enterprises A, B, and C determine which computing resources are available for resource pooling and sharing services, and the cloud coordinator 110 in turn guides clients W, X, Y, and Z to use the available computing resources.

[0029] In some embodiments, the computing resources and servers of the participating enterprises may be located in one or more data centers. Figure 2 Conceptually illustrates an enterprise that supports resource consolidation and shared services (e.g., Figure 1 The enterprise data center 200 implements an enterprise partition 210 and a backup partition 220.

[0030] As shown, enterprise partition 210 includes computing resources or racks of servers interconnected by switches and routers. The computing resources of enterprise partition 210 are behind firewall 215, which allows the computing resources of enterprise partition 210 to be accessed by the enterprise operating or owning the data center. Firewall 215 only allows the authenticated users of the enterprise, thereby preventing entities outside the enterprise from accessing enterprise partition 210, including the client of resource pooling and shared services. Standby partition 220 also includes computing resources or racks of servers interconnected by switches and routers. The computing resources of standby partition 220 are behind firewall 225, which allows the authenticated clients of resource pooling and shared services to access computing resources. The enterprise workload manager 230 of the enterprise is located in the enterprise partition behind firewall 215.

[0031] In some embodiments, the data center maintains a physical separation or "air gap" between the enterprise partition 210 and the standby partition 220. The "air gap" is used to physically prevent entities outside the enterprise (e.g., clients of resource consolidation and sharing services) from accessing computing resources in the enterprise partition. Specifically, within the data center 200, there is no communication medium between the hardware devices (e.g., servers, racks, switches, routers) of the enterprise partition 210 and the hardware devices of the standby partition 220. In some embodiments, the enterprise partition 210 has no physical connection or contact with the hardware devices of the standby partition 220. Therefore, one way to move a computing resource or server from an enterprise partition is to physically disconnect the computing resource from the hardware connection of the enterprise partition and physically connect the computing resource to the hardware connection of the standby partition. Similarly, the only way to move a computing resource or server from a standby partition to an enterprise partition is to physically disconnect the computing resource from the hardware connection of the standby partition and physically connect the computing resource to the hardware connection of the enterprise partition.

[0032] In the enterprise data center 200, the robot 250 is employed at the enterprise data center to physically connect and disconnect computing resources. For example, to transfer the computing resource 240 from the enterprise partition 210 to the standby partition 220, the enterprise workload manager 230 may instruct the robot 250 to perform the following operations: (1) unplug the cable between the computing resource 240 and the switch of the enterprise partition 210, and (2) plug in the cable between the computing resource 240 and the switch of the standby partition 220.

[0033] In some other embodiments, the data center of an enterprise participating in the cloud enterprise resource management system 100 does not use physical policies to transfer computing resources between the enterprise partition and the standby partition. Instead, the transferred computing resources remain physically connected to both the enterprise partition and the standby partition, and the transfer is completed virtually in the electronic signaling domain, for example, by changing the authentication requirements for the computing resources or imposing other software security measures to separate the enterprise partition and the standby partition.

[0034] The enterprise workload manager 230 initiates the transfer of computing resources between the enterprise partition and the standby partition based on its prediction of the enterprise workload at the data center. In some embodiments, a predicted workload that is sufficiently below the capacity of the enterprise partition (e.g., below a first threshold) is used as an indication that one or more computing resources can be moved from the enterprise partition to the standby partition. Conversely, a predicted workload that is within a safety margin of the capacity of the enterprise partition or exceeds the capacity of the enterprise partition (e.g., above a second threshold) is used as an indication that one or more computing resources need to be moved from the standby partition back to the enterprise partition. The enterprise workload manager 230 may, in turn, (1) notify the cloud coordinator 110 that computing resources are available for resource consolidation and sharing services, or (2) request the cloud coordinator 110 to release computing resources from the resource consolidation and sharing services. Figure 3a -b conceptually illustrates the sequence of events at the enterprise workload manager 230 and the cloud orchestrator 110 when transferring computing resources between an enterprise partition and a standby partition.

[0035] Figure 3a The sequence of operations for transferring computing resources from an enterprise partition 210 to a standby partition 220 is shown. As shown, the workload manager 230 determines (at step 310) that resources are available for resource pooling and sharing services, for example, when the predicted enterprise workload is below a certain threshold. The workload manager identifies (at step 311) computing resources that are available for the cloud. The workload manager 230 then instructs (at step 312) the robot 250 to move the identified resources from the enterprise partition 210 to the standby partition 220. The workload manager then notifies (at step 313) the cloud coordinator 110 that the resources are being used for resource pooling and sharing services. The workload manager also sends (at step 314) the identification of the resources that are becoming available to the cloud coordinator 110. The cloud coordinator in turn notifies (at step 315) the workload manager that the resources are accepted and that the clients of the resource pooling and sharing services can be directed to use the resources.

[0036] Figure 3bAn example sequence of operations for transferring computing resources from a standby partition 220 back to an enterprise partition 210 is shown. As shown, the workload manager 230 determines (at step 320) that the enterprise may need additional computing resources at a future time, such as when the predicted enterprise workload is above a certain threshold. The workload manager requests (at step 321) the cloud coordinator 110 to release the computing resources in the standby partition 220 at a specified time frame. The cloud coordinator 110 in turn identifies (at step 322) the computing resources in the standby partition 220 to be released at the specified time, and provides (at step 323) the identity of the released computing resources. The cloud coordinator can identify the computing resources that are not used by any client of the resource pooling and sharing service for release. The workload manager in turn notifies (at step 324) the cloud coordinator that the released resources are accepted. The workload manager also instructs (at step 325) the robot 250 to move the identified resources from the standby partition 220 to the enterprise partition 210.

[0037] In some embodiments, if all computing resources in the standby partition 220 are being used by clients of the resource pooling and sharing service, the cloud coordinator 110 may notify the workload manager 230 that all computing resources in the standby partition 220 are being used. In these instances, the cloud coordinator 110 may wait until at least one computing resource in the standby partition 220 becomes idle before notifying (at step 324) the workload manager 230 of the identity of the computing resources to be released back to the enterprise partition.

[0038] Figure 4a A block diagram of an enterprise workload manager 230 according to an exemplary embodiment is conceptually shown. The enterprise workload manager 230 is implemented on an example computing device 410. The computing device 410 implements a workload predictor 411, a resource status table 412, a workload controller 413, a cloud interface 414, and a transport interface 415. In some embodiments, modules 411-. In some embodiments, modules 411-415 are modules of hardware circuits implemented by one or more integrated circuits (ICs) of an electronic device. Although modules 411-415 are illustrated as separate modules, some modules may be combined into a single module. Reference will be made to Figure 6 An example computing device 600 is described in which computing device 410 may be implemented.

[0039] The workload predictor 411 predicts the workload that will occur in the enterprise partition 210 in the future time frame. The workload predictor can make this prediction based on historical workload records, measurements or key performance indicators taken from the data center, and data provided by the enterprise operating the data center. The predicted workload is provided to the workload controller 413.

[0040] The workload controller 413 makes decisions about whether to move resources into or out of the standby partition based on the predicted workload provided by the workload predictor 411. The resource status table 412 maintains the status of different computing resources in the data center. The status of the computing resource may include whether the computing resource is currently in the enterprise partition 210 or in the standby partition 220, whether the computing resource is used by enterprise activities, whether the computing resource is used by client activities of resource merging and sharing services, etc. The workload controller 413 can use the contents of the resource status table 412 to determine the current capacity of the enterprise partition. Based on the comparison between the determined current enterprise partition capacity and the predicted workload, the workload controller 413 can (1) require additional capacity to be added to the enterprise partition or (2) allow excess capacity to be moved to the standby partition. The workload controller 413 can achieve the corresponding transmission of computing resources by using the cloud interface 414 and the transmission interface 415.

[0041] The cloud interface 414 is used by the workload manager computing device 410 to communicate with the cloud coordinator 110 to (1) provide notification that computing resources are becoming available, (2) request that computing resources be released back to the enterprise partition, (3) identify the computer resources being transferred, and (4) determine the timing of when the transfer can occur. The communication between the cloud interface 414 and the cloud coordinator 110 is described above with reference to the above Figure 3a -b. Cloud interface 414 may include the communication hardware and software components necessary to communicate over the Internet.

[0042] The transport interface 415 controls the actual transfer of computing resources between the enterprise partition 210 and the spare partition 220. In some embodiments, the transport interface 415 converts commands from the workload controller into specific instructions for controlling a robot (e.g., robot 250) to physically disconnect computing resources and / or connect computing resources to an enterprise partition or a spare partition.

[0043] Figure 4b A block diagram of a cloud orchestrator 110 according to an exemplary embodiment is conceptually shown. The cloud orchestrator 110 is implemented on an example computing device 420. The computing device 420 implements an enterprise interface 421, a resource status table 422, a service controller 423, a client interface 424, and an accounting module 425. In some embodiments, modules 421-425 are modules of software instructions executed by one or more processing units (e.g., processors) of the computing device 410. In some embodiments, modules 421-425 are modules of hardware circuits implemented by one or more integrated circuits (ICs) of an electronic device. Although modules 421-425 are illustrated as separate modules, some of these modules may be combined into a single module. Reference will be made to Figure 6 An example computing device 600 is described in which computing device 420 may be implemented.

[0044] The enterprise interface 421 is used by the cloud coordinator to communicate with the data centers of enterprises participating in resource pooling and sharing services. The communication between the enterprise interface 421 and the participating enterprises is referred to above. Figure 3a -b describes.

[0045] The service controller 423 determines which computing resource is provided to which client of the resource pooling and sharing service. The service controller 423 learns from the enterprise interface 421 which computing resources are available in which enterprise data center (i.e., in the backup partition), and which enterprise is requesting to release computing resources from the resource pooling and sharing service. The state of each computing resource is maintained in the resource state table 422, and the service controller 423 uses the contents of the resource state table 422 to determine whether to allocate the computing resource to the client or release the computing resource back to the enterprise.

[0046] Resource status table 422 maintains the status of different computing resources of different enterprise data centers participating in resource pooling and sharing services. The status of computing resources may include an identifier of the data center that houses the computing resources, a flag indicating whether the computing resources are available to clients of resource pooling and sharing services, a flag indicating whether the computing resources are currently being used by clients of resource pooling and sharing services, the identity of the client that is using the computing resources, etc.

[0047] Client interface 424 is used to communicate with various clients of the resource pooling and sharing service.The information relayed to the client may include the identity or address of the computing resources made available to the client, as well as authentication information that is significant for the client to access the computing resources in its corresponding enterprise data center.

[0048] The billing module 425 maintains a database of accounts of clients and participating enterprises. The billing module 425 monitors the use of computing resources in participating enterprises by clients of resource pooling and sharing services. For example, the billing module can credit the enterprise's account and debit the client's account based on the monitored use of computing resources by the client.

[0049] Figure 5 Conceptually, a process 500 for controlling a data center participating in resource pooling and sharing services according to an exemplary embodiment is shown. In some embodiments, one or more processing units (e.g., processors) of a computing device (e.g., computing device 410) implementing enterprise workload manager 230 perform process 500 by executing instructions stored in a computer-readable medium.

[0050] The workload manager predicts (at block 510) a workload at a data center that includes a first partition of computing resources and a second partition of computing resources. In some embodiments, the first partition of computing resources (the enterprise partition) is designated as reserved by the enterprise, while the second partition of computing resources (the spare partition) is designated as available for use by one or more clients of a resource pooling and sharing service that coordinates one or more clients to share computing resources belonging to one or more enterprises. In some embodiments, access to the first partition is controlled by a first firewall that allows access by the enterprise and denies access by clients of the resource pooling and sharing service or any entity external to the enterprise.

[0051] The workload manager determines (at block 520) whether the predicted workload is below a first threshold. If the predicted workload is below the first threshold, the process proceeds to block 525. Otherwise, the process proceeds to block 530. In some embodiments, a predicted workload that is sufficiently below the capacity of the enterprise partition (e.g., below the first threshold) is used as an indication that excess computing capacity exists in the enterprise partition, and one or more computing resources can be moved from the enterprise partition to the standby partition.

[0052] At box 525, the workload manager transfers the first computing resource from the first partition to the second partition. The process then returns to box 510 to further predict the workload of the enterprise at the data center. In some embodiments, the workload manager instructs the robot to physically disconnect the first computing resource from the first partition and physically connect the first computing resource to the second partition. The workload manager can also communicate with a cloud coordinator of resource pooling and sharing services to provide an identifier of the first computing resource and specify a time frame in which the first computing resource becomes available for use by a client of the resource pooling and sharing services. The cloud coordinator can then support the client of the resource pooling and sharing services to access the first computing resource by using the provided identifier of the first computing resource at a specified time frame. The cloud coordinator also credits the account of the enterprise and debits the account of the client based on the use of the first computing resource.

[0053] The workload manager determines (at block 530) whether the predicted workload is above a second threshold. If the predicted workload is above the second threshold, the process proceeds to block 535. Otherwise, the process returns to block 510. In some embodiments, the predicted workload being within the fault tolerance of the capacity of the enterprise partition or exceeding the capacity of the enterprise partition (e.g., above the second threshold) is used as an indication that there is insufficient computing capacity in the enterprise partition so that one or more computing resources are moved from the standby partition back to the enterprise partition.

[0054] At box 535, the workload manager transfers the second computing resource from the second partition to the first partition. The process then returns to box 510 to further predict the workload of the enterprise at the data center. In some embodiments, the workload manager instructs the robot to physically disconnect the second computing resource from the second partition and physically connect the second computing resource to the first partition. The workload manager can also communicate with the cloud coordinator of the resource pooling and sharing service to request the return or release of the computing resource and receive a reply including an identifier of the computing resource being returned or released. The identifier can identify a computing resource in the second partition that is not used by any client of the resource pooling and sharing service.

[0055] By predicting the workload in the enterprise data center, the computing device acting as the workload manager of the enterprise data center can participate in resource pooling and sharing services and enable the cloud-enterprise resource management system. Based on the predicted workload, the computing device dynamically determines whether to provide spare resources to the resource pooling and sharing services or request to release resources from the resource pooling and sharing services. The resource utilization efficiency of the enterprise data center is improved.

[0056] The present application may be a system, method and / or computer program product at any possible level of technical detail integration. The computer program product may include a computer-readable storage medium (or multiple media) having computer-readable program instructions thereon, the computer-readable program instructions being used to cause a processor to perform aspects of the present disclosure.

[0057] A computer-readable storage medium may be a tangible device capable of retaining and storing instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device such as a punch card or a raised structure in a groove on which instructions are recorded, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be interpreted as a temporary signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (e.g., a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0058] The computer-readable program instructions described herein can be downloaded to the corresponding computing / processing equipment from the computer-readable storage medium, or via a network, such as the Internet, a local area network, a wide area network and / or a wireless network, downloaded to an external computer or an external storage device. The network can include copper transmission cables, optical transmission optical fibers, wireless transmissions, routers, firewalls, switches, gateway computers and / or edge servers. The network adapter card or the network interface in each computing / processing equipment receives the computer-readable program instructions from the network, and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in the corresponding computing / processing equipment. The computer-readable program instructions for performing the operation disclosed herein can be assembly instructions, instruction set architecture (ISA) instructions, machine-related instructions, microcodes, firmware instructions, state setting data, configuration data of integrated circuits, or source code or object code written in any combination of one or more programming languages ​​(including object-oriented programming languages, such as Smalltalk, C++, etc.) and procedural programming languages ​​(such as "C" programming languages ​​or similar programming languages). Computer readable program instructions can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider through the Internet). In some embodiments, electronic circuits including, for example, programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) can execute computer readable program instructions to personalize the electronic circuits by utilizing the state information of computer readable program instructions, so as to perform aspects of the present disclosure.

[0059] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It will be understood that each frame of the flowchart and / or block diagram and the combination of frames in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a computer or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device create a device for implementing the function / action specified in one or more frames of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can guide a computer, a programmable data processing device and / or other equipment to work in a particular manner, so that the computer-readable storage medium having instructions stored therein includes a product, and the product includes instructions for implementing various aspects of the function / action specified in one or more frames of the flowchart and / or block diagram.

[0060] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operation steps are performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable apparatus, or other device implement the functions / actions specified in one or more blocks of the flowchart and / or block diagram. Figure 5 ) in the flowchart and block diagrams show the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, segment or part of an instruction, which includes one or more executable instructions for implementing a specified logical function. In some alternative embodiments, the functions noted in the box may not occur in the order noted in the figure. For example, two boxes shown in succession can actually be executed substantially simultaneously, or these boxes can sometimes be executed in reverse order, depending on the functions involved. It will also be noted that each box illustrated in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can be implemented by a dedicated hardware-based system that performs a specified function or action or performs a combination of dedicated hardware and computer instructions.

[0061] Figure 6 1 is a block diagram of components of data processing systems 600 and 650 that may be used to implement a workload manager (e.g., 230) or a cloud orchestrator (e.g., 110) according to an illustrative embodiment of the present disclosure. It should be understood that Figure 6 This merely provides an illustration of one implementation and does not imply any limitation on the environments in which different embodiments may be implemented. Many modifications to the described environment may be made based on design and implementation requirements.

[0062] Data processing systems 600 and 650 represent any electronic device capable of executing machine-readable program instructions. Data processing systems 600 and 650 may represent a smart phone, a computer system, a PDA, or other electronic device. Examples of computing systems, environments, and / or configurations that may be represented by data processing systems 600 and 650 include, but are not limited to, personal computer systems, server computer systems, thin clients, fat clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, network PCs, minicomputer systems, and distributed cloud computing environments that include any of the above systems or devices.

[0063] Data processing systems 600 and 650 may include Figure 6 , and a set of internal components 605 and a set of external components 655, the set of internal components 605 includes one or more processors 620, one or more computer-readable RAMs 622, and one or more computer-readable ROMs 624 on one or more buses 626, as well as one or more operating systems 628 and one or more computer-readable tangible storage devices 630. One or more operating systems 628 and programs such as those used to perform process 500 are stored on one or more computer-readable tangible storage devices 630 for execution by one or more processors 620 via one or more RAMs 622 (which typically include cache memory). Figure 6 In the embodiment shown in , each of the computer-readable tangible storage devices 630 is a disk storage device of an internal hard drive. Alternatively, each computer-readable tangible storage device 630 is a semiconductor memory device such as ROM 624, EPROM, flash memory, or any other computer-readable tangible storage device capable of storing computer programs and digital information.

[0064] The collection of internal components 605 also includes an R / W drive or interface 632 to read from and write to one or more portable computer-readable tangible storage devices 686, such as a CD-ROM, DVD, memory stick, tape, disk, optical disk, or semiconductor storage device. Instructions for performing process 500 may be stored on one or more of the respective portable computer-readable tangible storage devices 686, read via the respective R / W drive or interface 632, and loaded into the respective hard disk drive 630.

[0065] The collection of internal components 605 may also include a network adapter (or switch port card) or interface 636, such as a TCP / IP adapter card, a wireless Wi-Fi interface card, or a 3G or 4G wireless interface card or other wired or wireless communication link. The instructions of the above-mentioned process or program can be downloaded from an external computer (e.g., a server) via a network (e.g., the Internet, a local area network or other wide area network) and a corresponding network adapter or interface 636. From the network adapter (or switch port adapter) or interface 636, the instructions and data of the described program or process are loaded into the corresponding hard disk drive 630. The network may include copper wire, optical fiber, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers.

[0066] The set of external components 655 may include a computer display monitor 670, a keyboard 680, and a computer mouse 684. The set of external components 655 may also include a touch screen, a virtual keyboard, a touch pad, a pointing device, and other human interface devices. The collection of internal components 605 also includes a device driver 640 to interface with the computer display monitor 670, the keyboard 680, and the computer mouse 684. The device driver 640, the R / W driver or interface 632, and the network adapter or interface 636 include hardware and software (stored in the storage device 630 and / or ROM 624).

[0067] It should be understood that although the present disclosure includes a detailed description of cloud computing, the implementation of the teachings set forth herein is not limited to a cloud computing environment. Instead, embodiments of the present disclosure can be implemented in conjunction with any other type of computing environment now known or later developed. Cloud computing is a service delivery model for enabling convenient on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be quickly provisioned and released with minimal management effort or interaction with the provider of the service. The cloud model may include at least five features, at least three service models, and at least four deployment models.

[0068] On-demand self-service: Cloud consumers can unilaterally and automatically provision computing capabilities, such as server time and network storage, as needed without requiring manual interaction with the service provider.

[0069] Wide Area Network Access: Capabilities are available over the network and accessed through standard mechanisms that support use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

[0070] Resource pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, where different physical and virtual resources are dynamically allocated and reallocated based on demand. There is location independence in the sense that consumers typically do not control or know the exact location of the resources provided, but are able to specify the location at a higher level of abstraction (e.g., country, state, or data center).

[0071] Rapid elasticity: In some cases, the ability to scale out quickly and in quickly can be provisioned quickly and elastically. To the consumer, the capacity available for provisioning often appears to be unlimited and can be purchased in any quantity at any time.

[0072] Metered Services: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both providers and consumers of the utilized services.

[0073] Software as a Service (SaaS): The capability provided to the consumer is to use the provider's applications running on the cloud infrastructure. The applications are accessible from a variety of client devices through a thin client interface such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure including the network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

[0074] Platform as a Service (PaaS): The capability provided to consumers is to deploy applications created or acquired by consumers using programming languages ​​and tools supported by the provider onto the cloud infrastructure. Consumers do not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but have control over deployed applications and possible configuration of the application hosting environment. Infrastructure as a Service (IaaS): The capability provided to consumers is to provide processing, storage, networking, and other basic computing resources on which consumers can deploy and run arbitrary software, which may include operating systems and applications. Consumers do not manage or control the underlying cloud infrastructure, but have control over operating systems, storage, deployed applications, and possibly limited control over selected networking components (e.g., host firewalls).

[0075] Private Cloud: The cloud infrastructure is operated only for the organization. It can be managed by the organization or a third party and can exist inside or outside the building.

[0076] Community cloud: The cloud infrastructure is shared by several organizations and supports a specific community with shared concerns (e.g., mission, security requirements, policies, and compliance considerations). It can be managed by the organization or a third party and can exist on-premises or off-premises.

[0077] Public cloud: Cloud infrastructure is available to the general public or large industrial groups and is owned by an organization that sells cloud services.

[0078] Hybrid cloud: A cloud infrastructure is a combination of two or more clouds (private, community or public) that remain a unique entity but are bound together by standardized or proprietary technologies that enable data and application portability (e.g., cloud bursting for load balancing between clouds).

[0079] The cloud computing environment is service-oriented, with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the core of cloud computing is an infrastructure consisting of a network of interconnected nodes.

[0080] Reference now Figure 7 , depicts an illustrative cloud computing environment 750. As shown, the cloud computing environment 750 includes one or more cloud computing nodes 710 with which a local computing device used by a cloud consumer can communicate, such as a personal digital assistant (PDA) or cellular phone 754A, a desktop computer 754B, a laptop computer 754C, and / or an automobile computer system 754N. The nodes 710 can communicate with each other. They can be physically or virtually grouped (not shown) in one or more networks, such as a private cloud, community cloud, public cloud, or hybrid cloud, or a combination thereof, as described above. This allows the cloud computing environment 750 to provide infrastructure, platform, and / or software as a service for which the cloud consumer does not need to maintain resources on a local computing device. It should be understood that Figure 7 The types of computing devices 754A-N shown in are illustrative only, and computing node 710 and cloud computing environment 750 may communicate with any type of computerized device over any type of network and / or network addressable connection (eg, using a web browser).

[0081] Reference now Figure 8 , showing that ( Figure 7 A set of functional abstraction layers provided by the cloud computing environment 750. It should be understood that Figure 8 The components, layers, and functions shown in are intended to be illustrative only, and embodiments of the present invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

[0082] The hardware and software layer 860 includes hardware and software components. Examples of hardware components include: host 861; server 862 based on RISC (Reduced Instruction Set Computer) architecture; server 863; blade server 864; storage device 865; and network and network components 866. In some embodiments, software components include network application server software 867 and database software 868.

[0083] Virtualization layer 870 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual servers 871 ; virtual storage 872 ; virtual networks 873 , including virtual private networks; virtual applications and operating systems 874 ; and virtual clients 875 .

[0084] In one example, the management layer 880 may provide the functionality described below. Resource provisioning 881 provides dynamic procurement of computing resources and other resources used to perform tasks within a cloud computing environment. Metering and pricing 882 provides cost tracking when resources are utilized within a cloud computing environment, as well as accounting or invoicing for the consumption of these resources. In one example, these resources may include application software licenses. Security provides authentication for cloud consumers and tasks, as well as protection for data and other resources. User portal 883 provides access to the cloud computing environment for consumers and system administrators. Service level management 884 provides cloud computing resource allocation and management so that the required service levels are met. Service level agreement (SLA) planning and fulfillment 885 provides pre-scheduling and procurement of cloud computing resources, where future demand is anticipated based on the SLA.

[0085] Workload layer 890 provides examples of functionality that can utilize a cloud computing environment. Examples of workloads and functionality that can be provided from this layer include: mapping and navigation 891; software development and lifecycle management 892; virtual classroom education delivery 893; data analysis processing 894; transaction processing 895; and workload 896. In some embodiments, workload 896 performs some operations of cloud orchestrator 110.

[0086] One or more of the foregoing embodiments implement a workload manager for resource pooling and shared services in a cloud-enterprise resource management system at an enterprise data center. The workload manager is implemented in a computer infrastructure by enabling one or more computing devices to perform workload prediction for a data center and move computing resources between enterprise partitions and backup partitions based on the predicted workload. The computer infrastructure is also used to communicate with a cloud coordinator for resource pooling and shared services, which can also be implemented in the computer infrastructure.

[0087] The description of various embodiments of the present disclosure has been presented for illustrative purposes, but is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein are selected to best explain the principles of the embodiments, practical applications, or technical improvements existing in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A system comprising: A cloud coordinator configured to coordinate the sharing of computing resources of a plurality of data centers belonging to an enterprise by one or more clients of a resource pooling and sharing service, wherein: Each enterprise's data center includes a first partition of computing resources and a second partition of computing resources. A first partition of the computing resources is designated as reserved for use by the enterprise, A second partition of the computing resources is designated as available for use by one or more clients of the resource pooling and sharing service, and A physical air gap exists between the first partition of computing resources and the second partition of computing resources; and Multiple workload managers where: Each workload manager is associated with a corresponding enterprise and is configured to predict a workload at a data center of the corresponding enterprise, Each workload manager is configured to (i) transfer a first computing resource from the first partition of the corresponding data center to the second partition of the data center when it is determined that the predicted workload is below a first threshold, and (ii) transfer a second computing resource from the second partition of the corresponding data center to the first partition of the data center when it is determined that the predicted workload is above a second threshold.

2. The system according to claim 1, wherein: The workload manager is configured to transfer the first computing resource from the first partition to the second partition by instructing a robot to physically disconnect the first computing resource from the first partition and physically connect the first computing resource to the second partition.

3. A computer-implemented method comprising: predicting a workload at a data center of an enterprise, the data center comprising a first partition of computing resources and a second partition of computing resources, wherein the first partition of computing resources is designated by a common workload manager of the data center as reserved for use by the enterprise and the second partition of computing resources is designated as available for use by one or more clients of a resource pooling and sharing service that coordinates sharing of computing resources belonging to one or more enterprises by one or more clients; When it is determined that the predicted workload is below a first threshold, transferring, by the workload manager of the data center, a first computing resource from the first partition to the second partition; and Upon determining that the predicted workload is above a second threshold, the workload manager of the data center transfers a second computing resource from the second partition to the first partition.

4. The computer-implemented method of claim 3, wherein: Transferring the first computing resource from the first partition to the second partition includes instructing a robot to physically disconnect the first computing resource from the first partition and physically connect the first computing resource to the second partition.

5. The computer-implemented method of claim 3, wherein: Transferring a first computing resource from the first partition to the second partition includes communicating with a cloud coordinator of the resource pooling and sharing service to provide an identifier of the first computing resource and specify a time frame in which the first computing resource becomes available for use by the client of the resource pooling and sharing service.

6. The computer-implemented method of claim 5, wherein: The cloud coordinator supports access to the first computing resource by a client of the resource pooling and sharing service using the provided identifier of the first computing resource within the specified time frame.

7. The computer-implemented method of claim 6, wherein: The cloud orchestrator credits an account of the enterprise and debits an account of the client based on usage of the first computing resource.

8. The computer-implemented method of claim 3, wherein: Transferring the second computing resource from the second partition to the first partition includes instructing a robot to physically disconnect the second computing resource from the second partition and physically connect the second computing resource to the first partition.

9. The computer-implemented method of claim 3, wherein: Transferring the second computing resource from the second partition to the first partition includes communicating with a cloud coordinator of the resource pooling and sharing service to request a return of a computing resource and receiving a reply including an identifier of the computing resource to be returned.

10. The computer-implemented method of claim 9, wherein: The identifier identifies a computing resource in the second partition that is not used by any client of the resource pooling and sharing service.

11. The computer-implemented method of claim 3, wherein: Access to the first partition is controlled by a first firewall that allows access by the enterprise and denies access by clients of the resource pooling and sharing service.

12. A computing device comprising: processor; as well as a storage device storing a set of instructions, wherein execution of the set of instructions by the processor configures the computing device to perform actions comprising: Predicting a workload at a data center of an enterprise, the data center comprising a first partition of computing resources and a second partition of computing resources having a common workload manager of the data center, wherein: The first partition of computing resources is designated as reserved for use by the enterprise, and The second partition of computing resources is designated as available to one or more clients of a resource pooling and sharing service that coordinates the sharing of computing resources belonging to one or more enterprises by the one or more clients; maintaining a physical air gap between the first partition of computing resources and the second partition of computing resources; When it is determined that the predicted workload is below a first threshold, transferring, by the workload manager of the data center, a first computing resource from the first partition to the second partition; and When it is determined that the predicted workload is above a second threshold, the workload manager of the data center will transfer a second computing resource from the second partition to the first partition.

13. The computing device of claim 12, wherein: Transferring the first computing resource from the first partition to the second partition includes instructing a robot to physically disconnect the first computing resource from the first partition and to physically connect the first computing resource to the second partition.

14. The computing device of claim 12, wherein: Transferring a first computing resource from the first partition to the second partition includes communicating with a cloud coordinator of the resource pooling and sharing service to provide an identifier of the first computing resource and specify a time frame in which the first computing resource becomes available for use by the client of the resource pooling and sharing service.

15. The computing device of claim 14, wherein: The cloud coordinator supports a client of the resource pooling and sharing service to access the first computing resource by using the provided identifier of the first computing resource during the specified time frame.

16. The computing device of claim 15, wherein: The cloud orchestrator credits an account of the enterprise and debits an account of the client based on usage of the first computing resource.

17. The computing device of claim 12, wherein: Transferring the second computing resource from the second partition to the first partition includes instructing a robot to physically disconnect the second computing resource from the second partition and physically connect the second computing resource to the first partition.

18. The computing device of claim 12, wherein: Transferring a second computing resource from the second partition to the first partition includes communicating with a cloud coordinator of the resource pooling and sharing service to request a return of a computing resource and receiving a reply including an identifier of the computing resource to be returned.

19. The computing device of claim 18, wherein: The identifier identifies a computing resource in the second partition that is not used by any client of the resource pooling and sharing service.

20. The computing device of claim 12, wherein: Access to the first partition is controlled by a first firewall that allows access by the enterprise and denies access by clients of the resource pooling and sharing service.

21. A computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions being executable by a processor to cause the processor to perform any step of the method according to any one of claims 3 to 11.

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