RESERVING RESOURCES IN AN ON-DEMAND COMPUTE ENVIRONMENT

• US 20140129721A1
• Filed: 01/14/2014
• Published: 05/08/2014
• Est. Priority Date: 03/16/2005
• Status: Active Grant

• Current Assignee: III Holdings 12, LLC (Intellectual Ventures LLC)
• Original Assignee: Adaptive Computing Enterprises, Inc. (Ala Services LLC)
• Inventors: JACKSON, David B.
• Granted Patent: US 10,333,862 B2
• Time in Patent Office: Days
• US Class Current: 709/226
• CPC Class Codes: G06F 11/2038 with a single idle spare processing component
• CPC Class Codes: G06F 15/173 using an interconnection network, e.g. matrix, shuffle, pyramid, star, snowflake
• CPC Class Codes: G06F 2209/5014 Reservation
• 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: G06F 9/5061 Partitioning or combining of resources
• CPC Class Codes: G06F 9/5072 Grid computing
• CPC Class Codes: G06F 9/5077 Logical partitioning of resources; Management or configuration of virtualized resources specific details on emulation or internal functioning of virtual machines G06F9/455
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G08C 17/00 Arrangements for transmitting signals characterised by the use of a wireless electrical link
• CPC Class Codes: H04L 47/70 Admission control; Resource allocation
• CPC Class Codes: H04L 47/80 Actions related to the user profile or the type of traffic
• CPC Class Codes: H04L 47/83 based on usage prediction
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/1001 for accessing one among a plurality of replicated servers
• CPC Class Codes: H04L 67/125 involving control of end-device applications over a network

Abstract

Disclosed is an on-demand system and method for managing resources in an on-demand compute environment from a local compute environment. The method includes receiving information at a local resource broker that is associated with resources within an on-demand compute environment, based on the information, communicating instructions from the local resource broker to the on-demand compute environment and modifying resources associated with the on-demand compute environment based on the instructions.

Claims

1. A system comprising:

a processor; and

a computer-readable storage medium storing code which, when executed by the processor, cause the processor to perform operations comprising;

receiving instructions from a local resource broker, wherein the instructions are associated with compute resources to be used to process user workload, wherein the local broker, an on-demand compute environment and a local compute environment are each separate environments, wherein a specification of resources associated with the instructions are one of fully explicit, partially explicit, fully implicit based on workload, and based on a virtual private cluster package concept where the virtual private cluster package can include aspects of provisioning a support environment and adjustments to resource request timeframes including pre-allocation, allocation duration, and post-allocation timeframe adjustments;

reserving, at a first time, compute resources in the on-demand compute environment based on the instructions to yield reserved compute resources for use at a second time later than the first time; and

receiving a transfer of the user workload from the local compute environment to the on-demand compute environment such that the user workload consumes the reserved compute resources in the on-demand compute environment at the second time, the user workload having been submitted to the local compute environment.

2. The system of claim 1, wherein the instructions comprise a request which is one of a request for a reservation of compute resources in the on-demand compute environment and a request for a modification of an existing reservation of compute resources in the on-demand compute environment.

3. The system of claim 2, wherein if the request is for a modification of an existing reservation of compute resources, then the step of reserving the compute resources within the on-demand compute environment further comprises modifying the existing reservation of compute resources.

4. The system of claim 3, wherein modifying the existing reservation of compute resources comprises one of expanding, reducing or canceling the existing reservation of compute resources.

5. The system of claim 1, wherein reserving the compute resources further comprises adjusting a reservation duration to accommodate for provisioning overhead associated with a reservation in the on-demand compute environment.

6. The system of claim 1, wherein the on-demand compute environment has an associated slave management module and the local compute environment has an associated master management module.

7. The system of claim 1, wherein the local resource broker locally creates a reservation for at least one aspect of a job, wherein other aspects of the job consume the reserved compute resources.

8. The system of claim 1, wherein the local resource broker has information that relates to resource requirement information based on one of a user specification, a current workload and a predicted workload.

9. The system of claim 1, wherein reserving the compute resources further comprises modifying compute resources for provisioning by customizing a delivered compute environment within the on-demand compute environment.

10. The system of claim 1, wherein reserving the compute resources further comprises co-allocating resources including at least two of a compute, network, storage, license, or service resources.

11. The system of claim 1, wherein reserving the compute resources further comprises co-allocating resources over disjoint timeframes.

12. A computer-readable storage device storing code which, when executed by a processor, cause the processor to perform operations comprising:

receiving instructions from a local resource broker, wherein the instructions are associated with compute resources to be used to process user workload, wherein the local broker, an on-demand compute environment and a local compute environment are each separate environments, wherein a specification of resources associated with the instructions are one of fully explicit, partially explicit, fully implicit based on workload, and based on a virtual private cluster package concept where the virtual private cluster package can include aspects of provisioning a support environment and adjustments to resource request timeframes including pre-allocation, allocation duration, and post-allocation timeframe adjustments;

reserving, at a first time, compute resources in the on-demand compute environment based on the instructions to yield reserved compute resources for use at a second time later than the first time; and

receiving a transfer of the user workload from the local compute environment to the on-demand compute environment such that the user workload consumes the reserved compute resources in the on-demand compute environment at the second time, the user workload having been submitted to the local compute environment.

13. The computer-readable storage device of claim 12, wherein the instructions comprise a request which is one of a request for a reservation of compute resources in the on-demand compute environment and a request for a modification of an existing reservation of compute resources in the on-demand compute environment.

14. The computer-readable storage device of claim 13, wherein if the request is for a modification of an existing reservation of compute resources, then the step of reserving the compute resources within the on-demand compute environment further comprises modifying the existing reservation of compute resources.

15. The computer-readable storage device of claim 14, wherein modifying the existing reservation of compute resources comprises one of expanding, reducing or canceling the existing reservation of compute resources.

16. The computer-readable storage device of claim 12, wherein reserving the compute resources further comprises adjusting a reservation duration to accommodate for provisioning overhead associated with a reservation in the on-demand compute environment.

17. The computer-readable storage device of claim 12, wherein the on-demand compute environment has an associated slave management module and the local compute environment has an associated master management module.

18. The computer-readable storage device of claim 12, wherein the local resource broker locally creates a reservation for at least one aspect of a job, wherein other aspects of the job consume the reserved compute resources.

19. The computer-readable storage device of claim 12, wherein the local resource broker has information that relates to resource requirement information based on one of a user specification, a current workload and a predicted workload.

20. A method comprising:

receiving instructions from a local resource broker, wherein the instructions are associated with compute resources to be used to process user workload, wherein the local broker, an on-demand compute environment and a local compute environment are each separate environments, wherein a specification of resources associated with the instructions are one of fully explicit, partially explicit, fully implicit based on workload, and based on a virtual private cluster package concept where the virtual private cluster package can include aspects of provisioning a support environment and adjustments to resource request timeframes including pre-allocation, allocation duration, and post-allocation timeframe adjustments;

reserving, at a first time, compute resources in the on-demand compute environment based on the instructions to yield reserved compute resources for use at a second time later than the first time; and

receiving a transfer of the user workload from the local compute environment to the on-demand compute environment such that the user workload consumes the reserved compute resources in the on-demand compute environment at the second time, the user workload having been submitted to the local compute environment.