GENERATING MONOTONE HASH PREFERENCES

• US 20130283287A1
• Filed: 04/04/2013
• Published: 10/24/2013
• Est. Priority Date: 08/14/2009
• Status: Abandoned Application

• Current Assignee: Qualcomm, Inc.
• Original Assignee: TransLattice, Inc.
• Inventors: Lyle, Michael P., Ross, Robert F.
• Application Number:
  US13/857,024
• Publication Number:
  US 20130283287A1
• Time in Patent Office: Days
• US Class Current: 718/104
• CPC Class Codes: G06F 9/50 Allocation of resources, e.g. of the central processing unit [CPU]
• CPC Class Codes: G06F 9/5033 considering data affinity

Abstract

Selecting a resource to fulfill a resource requirement is disclosed. For each resource requirement, a resource-specific affinity value is computed with respect to each of a plurality of resources. A bias is applied to each of at least a subset of the resource-specific affinity values. The biased, as applicable, resource-specific affinity values are sorted into a resource preference list. The sorted preference list is used to select a resource to fulfill the resource requirement.

Claims

1. A resource selection system, comprising:

a communication interface configured to receive a communication comprising an operation associated with a data object; and

a processor coupled to the communication interface and configured to;

compute for each of a plurality of resources, with respect to the data object, a resource-specific affinity value;

apply a bias to each of at least a subset of the resource-specific affinity values, wherein the bias comprises a function that is monotonically increasing; and

use the resource-specific affinity values to select a resource to fulfill the operation associated with the data object.

2. The system of claim 1, wherein the function is monotonically increasing in terms of one or more of an input hash and a desired bias.

3. The system of claim 1, wherein the bias comprises a resource-specific bias.

4. The system of claim 1, wherein the bias comprises a resource-specific bias determined for a particular resource based at least in part on a data associated with that particular resource.

5. The system of claim 1, wherein the bias is determined based at least in part on a data associated with the operation.

6. The system of claim 1, wherein the bias is determined for a particular resource based at least in part on a data reflecting a relationship between the data object and the particular resource.

7. The system of claim 6, wherein the bias is determined based at least in part on a stored data reflecting a past relationship between the resource and the data object.

8. The system of claim 6, wherein the bias is determined based at least in part on a stored data reflecting a past relationship between the resource and a user with which the operation is associated.

9. The system of claim 1, wherein the bias comprises a multiplicative bias.

10. The system of claim 1, wherein the operation is associated with storing the data object.

11. The system of claim 1, wherein the operation is associated with finding the data object in storage.

12. The system of claim 1, wherein the plurality of resources comprises a plurality of storage nodes.

13. The system of claim 1, wherein a consistent hash function is used to compute the resource-specific affinity values.

14. The system of claim 1, wherein the processor is further configured to:

detect that a removed resource has been removed from the plurality of resources; and

remove a resource-specific affinity value associated with the removed resource from the resource-specific affinity values.

15. The system of claim 1, wherein the processor is further configured to:

detect that an added resource has been added to the plurality of resources; and

compute a resource-specific affinity value for the added resource with respect to the data object.

16. A method of selecting a resource, comprising:

computing, using a processor, for each of a plurality of resources, with respect to the data object, a resource-specific affinity value;

applying a bias to each of at least a subset of the resource-specific affinity values, wherein the bias comprises a function that is monotonically increasing; and

using the resource-specific affinity values to select a resource to fulfill the operation associated with the data object.

17. The method of claim 16, wherein a consistent hash function is used to compute the resource-specific affinity values.

18. The method of claim 16, further comprising:

detecting that a removed resource has been removed from the plurality of resources; and

removing a resource-specific affinity value associated with the removed resource from the resource-specific affinity values.

19. The method of claim 16, further comprising:

detecting that an added resource has been added to the plurality of resources; and

computing a resource-specific affinity value for the added resource with respect to the data object.

20. A computer program product for selecting a resource, the computer program product being embodied in a computer readable storage medium and comprising computer instructions for:

computing for each of a plurality of resources, with respect to the data object, a resource-specific affinity value;

applying a bias to each of at least a subset of the resource-specific affinity values, wherein the bias comprises a function that is monotonically increasing; and

using the resource-specific affinity values to select a resource to fulfill the operation associated with the data object.