VIRTUAL WORLD SUBGROUP DETERMINATION AND SEGMENTATION FOR PERFORMANCE SCALABILITY

• US 20090271206A1
• Filed: 04/29/2008
• Published: 10/29/2009
• Est. Priority Date: 04/29/2008
• Status: Active Grant

• Current Assignee: Kyndryl Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Hamilton, Rick A. II, Oconnell, Brian M., Kozloski, James R., Pickover, Clifford A., Bhogal, Kulvir S.
• Granted Patent: US 8,230,441 B2
• Time in Patent Office: Days
• US Class Current: 705/1.100
• CPC Class Codes: G06F 9/5083 Techniques for rebalancing the load in a distributed system
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: H04L 41/0893 Assignment of logical groups to network elements
• CPC Class Codes: H04L 47/823 Prediction of resource usage
• CPC Class Codes: H04L 47/828 Allocation of resources per group of connections, e.g. per group of users
• 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

Abstract

A system and method of decreasing server loads and, more particularly, to decrease server load by automatically determining subgroups based on object interactions and computational expenditures. The system includes a plurality of servers; a subgroup optimization module configured to segment a plurality of objects into optimal subgroups; and a server transfer module configured to apportion one or more of the optimal subgroups between the plurality of servers based on a load of each of the plurality of servers. The method includes determining a relationship amongst a plurality of objects; segmenting the objects into optimized subgroups based on the relationships; and apportioning the optimized subgroups amongst a plurality of servers based on server load.

Claims

1. A system for reducing server loads, comprising:

a plurality of servers;

a subgroup optimization module configured to segment a plurality of objects into optimal subgroups; and

a server transfer module configured to apportion one or more of the optimal subgroups between the plurality of servers based on a load of each of the plurality of servers.

2. The system for reducing server loads of claim 1, wherein the subgroup optimization module further comprises:

a communication cost estimator which assigns a communication cost to one or more specific interactions that occur between the plurality of objects;

a computation cost estimator which determines object computation consumption between each of the plurality of servers; and

a segmentor which segments the plurality of objects into the optimal subgroups based on a result of the communication cost estimator and the computation cost estimator.

3. The system for reducing server loads of claim 2, wherein the communication cost estimator obtains a total communication cost from the specific interactions and weights the total communication cost according to one or more a predetermined criteria.

4. The system for reducing server loads of claim 2, wherein the computation cost estimator further determines server computation consumption and server computation consumption.

5. The system for reducing server loads of claim 4, wherein object computation consumption occurs internal to a virtual universe and server computation consumption occurs external to the virtual universe.

6. The system for reducing server loads of claim 4, wherein the computation cost estimator obtains a total computation cost on each of the plurality of objects by averaging the object computational consumption and the server computation consumption over an adjustable time period.

7. The system for reducing server loads of claim 2, wherein the server computation consumption is decreased by adjusting refresh rates between the plurality of servers such that one or more objects on a same server will refresh more frequently than one or more objects on a different server.

8. The system for reducing server load of claim 2, wherein the plurality of servers is optimized by unequal server loads on one or more servers of the plurality of servers.

9. The system for reducing server load of claim 2, wherein the optimal alignment is a function of a multiplier applied to a result calculated from the communication cost estimator and the computational cost estimator.

10. The system for reducing server load of claim 1, wherein the subgroup optimization module detects relationships amongst the plurality of objects and segments the plurality of objects into the optimal subgroups based on the relationships.

11. The system for reducing server load of claim 10, wherein the relationships include interactions between the plurality of objects.

12. The system for reducing server load of claim 10, wherein the relationships include current interactions between the plurality of objects.

13. The system for reducing server load of claim 1, wherein the subgroup optimization module is implemented on a computer infrastructure which is provided, maintained, deployed, and/or supported by a service provider.

14. The system for reducing server load of claim 1, wherein the plurality of objects are segmented into optimal subgroups based on one or more real-time communications between one or more of the plurality of objects, whereby the one or more real-time communications include chatting.

15. The system for reducing server load of claim 1, wherein the plurality of objects include at least one of a plurality of avatars and a plurality of assets.

16. A method for reducing server load, comprising:

determining a relationship amongst a plurality of objects;

segmenting the objects into optimized subgroups based on the relationships; and

apportioning the optimized subgroups amongst a plurality of servers based on server load.

17. The method for reducing server load of claim 16, wherein the segmenting comprises:

calculating a communication cost by assigning an interaction cost to one or more specific interactions that occur between the plurality of objects in order to determine the relationship; and

calculating a computation cost of an object computation consumption and a server computation consumption to determine the server load time.

18. The method for reducing server load of claim 17, further comprising averaging the interaction cost to determine the relationship between the plurality of objects.

19. The method for reducing server load of claim 16, further comprising dynamically adjusting refresh rates for different optimized subgroups based on the relationships amongst the plurality of objects.

20. The method for reducing server load of claim 16, wherein the relationships are current relationships.

21. A method for deploying an application for reducing server loads, comprising:

providing a computer infrastructure being operable to;

segment a plurality of objects into a plurality of subgroups; and

transfer one or more of the subgroups from a first server to at least a second server based on a determined load of the first server and the at least second server.

22. A method for deploying an application for reducing server loads of claim 21, wherein the computer infrastructure is operable to segment the plurality of objects into the plurality of subgroups based on relationships amongst the plurality of objects.

23. A method for deploying an application for reducing server loads of claim 21, wherein the computer infrastructure is operable to transfer the one or more subgroups based on a calculated server load of each server in a virtual universe environment.

24. A method for deploying an application for reducing server loads of claim 21, wherein the computer infrastructure is supported, maintained, provided, and deployed by a service provider on a fee and/or subscription basis.

25. A computer program embodied on a computer-readable medium having readable program code embodied in the medium, wherein the computer program product includes at least one component operable to:

determine a relationship amongst a plurality of objects;

segment the objects into optimized subgroups based on the relationships; and

apportion the optimized subgroups amongst a plurality of servers based on server load.