STATISTICAL PACKING OF RESOURCE REQUIREMENTS IN DATA CENTERS

• US 20100100877A1
• Filed: 10/16/2008
• Published: 04/22/2010
• Est. Priority Date: 10/16/2008
• Status: Active Grant

• Current Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Chu, Maurice, Hindi, Haitham, Parekh, Nitin, Greene, Daniel H., Preas, Bryan T.
• Granted Patent: US 8,656,404 B2
• Time in Patent Office: Days
• US Class Current: 718/1
• CPC Class Codes: G06F 2209/5019 Workload prediction
• CPC Class Codes: G06F 9/45533 Hypervisors; Virtual machine monitors
• CPC Class Codes: G06F 9/5011 the resources being hardware resources other than CPUs, Servers and Terminals
• CPC Class Codes: Y02D 10/00 Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

A computer-implemented method of managing resources in a virtual machine environment can include determining a specification of provisioning success corresponding to each of multiple jobs in the virtual machine environment, determining a prediction of resource needs corresponding to each of the jobs, and determining a resource specification corresponding to each of the jobs based on the specification of provisioning success and the prediction of resource needs.

Claims

1. A computer-implemented method of managing resources in a virtual machine environment, comprising:

determining a specification of provisioning success corresponding to each of a plurality of jobs in the virtual machine environment;

determining a prediction of resource needs corresponding to each of the plurality of jobs;

responsive to the specification of provisioning success and the prediction of resource needs, determining a resource specification corresponding to each of the plurality of jobs.

2. The computer-implemented method of claim 1, wherein determining the specification of provisioning success comprises deriving the specification of provisioning success from a corresponding service level agreement (SLA).

3. The computer-implemented method of claim 1, wherein determining the prediction of resource needs includes building a model based on historical data pertaining to past resource needs.

4. The computer-implemented method of claim 3, wherein predicting the resource needs comprises time of day conditioning.

5. The computer-implemented method of claim 3, wherein predicting the resource needs comprises Markov modeling.

6. The computer-implemented method of claim 1, wherein determining the resource specification corresponding to one of the plurality of jobs comprises determining additional resources that should be specified for the job that extend beyond resources specified for a group of jobs.

7. The computer-implemented method of claim 6, wherein the group of jobs comprises jobs specified previously in a priority order.

8. The computer-implemented method of claim 1, wherein the determined resource specification comprises a minimum amount of resources to be reserved for the corresponding one of the plurality of jobs.

9. The computer-implemented method of claim 1, further comprising:

repeating determining the prediction of resource needs; and

responsive to the repeated determined prediction of resource needs and the determined specification of provisioning success, adjusting a determined resource specification corresponding to one of the plurality of jobs.

10. The computer-implemented method of claim 1, further comprising providing the determined resource specification to a lower-level scheduling module.

11. One or more tangible computer-readable media storing thereon machine-executable instructions that, when executed by a processor, perform the computer-implemented method of claim 1.

12. A computer-implemented method of managing resources for at least one virtual machine in a virtual machine environment, comprising:

determining a specification of provisioning success corresponding to each of a plurality of jobs in the virtual machine environment;

forming a prioritized listing of the plurality of jobs; and

responsive to the specification of provisioning success and the prioritized listing, providing a resource specification for each of the plurality of jobs, wherein the providing comprises;

determining a first prediction of resource needs corresponding to each of a first subset of the plurality of jobs; and

determining a second prediction of resource needs corresponding to a second subset of the plurality of jobs.

13. The computer-implemented method of claim 12, wherein the first subset of the plurality of jobs comprises jobs having a resource specification that meets or exceeds a specified severity level.

14. The computer-implemented method of claim 12, further comprising providing a first resource specification corresponding to each of the first subset of the plurality of jobs.

15. The computer-implemented method of claim 14, further comprising providing a second resource specification corresponding to a resource pool corresponding to the second subset of the plurality of jobs.

16. The computer-implemented method of claim 15, wherein the resource pool comprises an amount of resources remaining after the first resource specification corresponding to each of the first subset of the plurality of jobs has been provided.

17. The computer-implemented method of claim 12, wherein the second prediction of resource needs meets or exceeds a specified probability threshold.

18. A computer-implemented method of managing resources for at least one virtual machine, comprising:

determining a failure probability for a plurality of jobs corresponding to the at least one virtual machine;

determining a prediction of resource needs corresponding to each of the plurality of jobs;

generating a total prediction of resource needs representing a sum of each prediction of resource needs; and

determining whether the total prediction of resource needs is below a specified threshold.

19. The computer-implemented method of claim 18, wherein, responsive to determining that the total prediction of resource needs is below the specified threshold, generating a resource specification corresponding to each of the plurality of jobs.

20. The computer-implemented method of claim 18, wherein, responsive to determining that the total prediction of resource needs meets or exceeds the specified threshold, adjusting the specified threshold.

21. The computer-implemented method of claim 20, further comprising repeating determining whether the total prediction of resource needs is below the adjusted specified threshold.

22. The computer-implemented method of claim 21, wherein, responsive to determining that the total prediction of resource needs is below the adjusted specified threshold, generating a resource specification corresponding to each of the plurality of jobs.

23. The computer-implemented method of claim 21, wherein, responsive to determining that the total prediction of resource needs meets or exceeds the adjusted specified threshold, adjusting the specified threshold.

24. The computer-implemented method of claim 18, further comprising determining an initial resource test interval, wherein the specified threshold is at about a halfway point within the initial resource test interval.