On-demand access to compute resources

• US 10,277,531 B2
• Filed: 07/06/2015
• Issued: 04/30/2019
• Est. Priority Date: 04/07/2005
• Status: Active Grant

• Current Assignee: III Holdings 12, LLC (Intellectual Ventures LLC)
• Original Assignee: III Holdings 2, LLC
• Inventors: Jackson, David Brian
• Primary Examiner(s): Naji, Younes
• Application Number:
  US14/791,873
• Publication Number:
  US 20150326500A1
• Time in Patent Office: 1,394 Days
• Field of Search: 709223
• CPC Class Codes: G06F 2209/509 Offload
• CPC Class Codes: G06F 9/5027 the resource being a machine, e.g. CPUs, Servers, Terminals
• CPC Class Codes: H04L 12/56 Packet switching systems
• CPC Class Codes: H04L 47/827 Aggregation of resource allocation or reservation requests
• CPC Class Codes: H04L 47/83 based on usage prediction
• CPC Class Codes: H04L 67/1001 for accessing one among a plurality of replicated servers
• CPC Class Codes: H04L 67/1008 based on parameters of servers, e.g. available memory or workload monitoring of computer activity G06F11/30
• CPC Class Codes: H04L 9/40 Network security protocols

Abstract

Disclosed are systems, methods and computer-readable media for controlling and managing the identification and provisioning of resources within an on-demand center as well as the transfer of workload to the provisioned resources. One aspect involves creating a virtual private cluster within the on-demand center for the particular workload from a local environment. A method of managing resources between a local compute environment and an on-demand environment includes detecting an event associated with a local compute environment and based on the detected event, identifying information about the local environment, establishing communication with an on-demand compute environment and transmitting the information about the local environment to the on-demand compute environment, provisioning resources within the on-demand compute environment to substantially duplicate the local environment and transferring workload from the local-environment to the on-demand compute environment. The event can be a threshold or a triggering event within or outside of the local environment.

Claims

1. A method comprising:

based on a detected event associated with a local compute environment, wherein the local compute environment is separate from a remote compute environment, receiving information about the local compute environment at the remote compute environment;

receiving a request for resources in the remote compute environment, wherein a specification of resources is associated with the request, the specification of resources being based on a virtual private cluster package, where the virtual private cluster package comprises provisioning a support environment and adjustments to resource request timeframes including pre-allocation, allocation duration, and post-allocation timeframe adjustments;

based on the information and the specification of resources, reserving compute resources in the remote compute environment for a specific workload, to yield reserved compute resources, and wherein the reserved compute resources comprise at least one processor;

provisioning the reserved compute resources within the remote compute environment, wherein a configuration of the remote compute environment substantially duplicates a configuration of the local compute environment; and

receiving the specific workload from the local compute environment to the reserved compute resources in the remote compute environment, wherein the specific workload consumes the reserved compute resources.

2. The method of claim 1, wherein the detected event is at least one of the passing of a threshold related to performance of the local compute environment associated with workload and a triggering event.

3. The method of claim 1, wherein the information about the local compute environment is related to at least one of:

hardware and an operating system.

4. The method of claim 1, wherein the detected event is a one-click step performed by a user.

5. The method of claim 1, wherein provisioning the reserved compute resources within the remote compute environment further comprises:

if a particular resource cannot be duplicated in the remote compute environment, then identifying and selecting a substitute resource.

6. The method of claim 5, wherein identifying and selecting a substitute resource is accomplished either at the remote compute environment or via negotiation between a slave workload manager at the remote compute environment and a master workload manager on the local compute environment.

7. The method of claim 1, wherein receiving the specific workload from the local compute environment at the remote compute environment further comprises only receiving an identified type of workload at the remote compute environment.

8. The method of claim 7, wherein receiving the identified type of workload at the remote compute environment is based upon one of different hardware and different software capabilities between the remote compute environment and the local compute environment.

9. The method of claim 1, wherein provisioning the reserved compute resources within the remote compute environment further comprises creating a virtual private cluster in the remote compute environment.

10. The method of claim 9, wherein the virtual private cluster further comprises an aggregate of compute resources in the remote compute environment having partitions and wherein only partitioned resources are accessible by the local compute environment.

11. The method of claim 1, further comprising:

receiving at least one profile as part of the information transmitted to the remote compute environment, wherein the profile is associated with other workload to be processed in the local compute environment; and

provisioning the reserved compute resources at least in part based on the profile.

12. The method of claim 1, wherein the detected event is a backlog workload condition in the local compute environment yielding a backlog workload, wherein the information received at the remote compute environment comprises an analysis of the backlog workload, the method further comprising:

provisioning the reserved compute resources in the remote compute environment according to the analysis to yield provisioned resources; and

receiving the backlog workload to the provisioned resources in the remote compute environment.

13. The method of claim 12, wherein provisioning the remote compute environment further comprises creating a virtual private cluster within the remote compute environment.

15. The method of claim 1, wherein provisioning the reserved compute resources within the remote compute environment comprises modifying flexible attributes of the reserved compute resources within the remote compute environment. more of a terrorist attack, pre- determined weather conditions, and power outages.

16. The method of claim 1, wherein the detected event is a wait time reaching a threshold.

17. The method of claim 1, wherein the detected event is one or more of a terrorist attack, pre-determined weather conditions, and power outages.

14. The method of 12, wherein the analysis comprises a determination of at least one resource type associated with the backlog workload for provisioning in the remote compute environment.

18. A system comprising:

a processor; and

a computer-readable storage medium storing instructions which, when executed by the

processor, cause the processor to perform operations comprising;

based on a detected event associated with a local compute environment, wherein the local compute environment is separate from a remote compute environment, receiving information about the local compute environment at the remote compute environment;

receiving a request for resources in the remote compute environment, wherein a specification of resources is associated with the request, the specification of resources being based on a virtual private cluster package, where the virtual private cluster package comprises provisioning a support environment and adjustments to resource request timeframes including pre-allocation, allocation duration, and post-allocation timeframe adjustments;

based on the information and the specification of resources, reserving compute resources in the remote compute environment for a specific workload, to yield reserved compute resources, and wherein the reserved compute resources comprise at least one processor;

provisioning the reserved compute resources within the remote compute environment, wherein a configuration of the remote compute environment duplicates a configuration of the local compute environment; and

receiving the specific workload from the local compute environment to the reserved compute resources in the remote compute environment, wherein the specific workload consumes the reserved compute resources.

19. A non-transitory computer-readable storage device storing instructions, which when executed by a processor, cause the processor to perform operations comprising:

based on a detected event associated with a local compute environment, wherein the local compute environment is separate from a remote compute environment, receiving information about the local compute environment at the remote compute environment;

receiving a request for resources in the remote compute environment, wherein a specification of resources is associated with the request, the specification of resources being based on a virtual private cluster package, where the virtual private cluster package comprises provisioning a support environment and adjustments to resource request timeframes including pre-allocation, allocation duration, and post-allocation timeframe adjustments;

based on the information and the specification of resources, reserving compute resources in the remote compute environment for a specific workload, to yield reserved compute resources, and wherein the reserved compute resources comprise at least one processor;

provisioning the reserved compute resources within the remote compute environment, wherein a configuration of the remote compute environment duplicates a configuration of the local compute environment; and

receiving the specific workload from the local compute environment to the reserved compute resources in the remote compute environment, wherein the specific workload consumes the reserved compute resources.