Selecting grid executors via a neural network

• US 20070005530A1
• Filed: 05/26/2005
• Published: 01/04/2007
• Est. Priority Date: 05/26/2005
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Wisniewski, Robert, Stecher, John, Duggirala, Surya, Baartman, Randall, Branda, Steven
• Application Number:
  US11/138,938
• Publication Number:
  US 20070005530A1
• Time in Patent Office: Days
• US Class Current: 706/16
• CPC Class Codes: G06F 9/5066 Algorithms for mapping a plurality of inter-dependent sub-tasks onto a plurality of physical CPUs mappping at compile time, see G06F8/451

Abstract

A method, apparatus, system, and signal-bearing medium that, in an embodiment, send units of work to grid executors, create training data based on the performance of the grid executors, and train a neural network via the training data. The training data includes pairs of input and output data, where the input data is the types of the units of work and the output data is the service strengths of the grid executors. Once the neural network has been trained, subsequent units of work have their grid executors selected by inputting the types of the units of work to the neural network and receiving a service strength from the neural network as output. The grid executors are then selected based on the output service strength from the neural network. In this way, in an embodiment, the grid performance may be increased.

Claims

1. A method comprising:

sending a first plurality of units of work to a first plurality of grid executors in parallel;

creating training data based on performance of the first plurality of grid executors;

training a neural network via the training data; and

selecting a second plurality of grid executors via the neural network.

2. The method of claim 1, further comprising:

sending a second unit of work to the second plurality of grid executors in parallel.

3. The method of claim 1, further comprising:

receiving a service strength from each of the first plurality of grid executors.

4. The method of claim 3, wherein the creating the training data further comprises:

creating a plurality of pairs of input data and output data based on the performance, wherein the input data comprises a plurality of types of the first plurality of units of work and the output data comprises the service strengths of the first plurality of grid executors.

5. The method of claim 4, wherein the creating the training data further comprises:

selecting the plurality of types based on response time for the plurality of types at the first plurality of grid executors.

6. The method of claim 2, wherein the selecting further comprises:

inputting a type of the second unit of work to the neural network; and

receiving a second service strength from the neural network.

7. The method of claim 6, wherein the selecting further comprises:

selecting the second plurality of grid executors based on the second service strength from the neural network.

8. A signal-bearing medium encoded with instructions, wherein the instructions when executed comprise:

receiving a service strength from each of a first plurality of grid executors;

selecting a subset of the first plurality of grid executors based on the service strength;

sending a first plurality of units of work to the subset of the first plurality of grid executors in parallel;

creating training data based on performance of the subset of the first plurality of grid executors;

training a neural network via the training data; and

selecting a second plurality of grid executors via the neural network.

9. The signal-bearing medium of claim 8, further comprising:

sending a second unit of work to the second plurality of grid executors in parallel.

10. The signal-bearing medium of claim 8, wherein the creating the training data further comprises:

creating a plurality of pairs of input data and output data based on the performance, wherein the input data comprises a plurality of types of the first plurality of units of work and the output data comprises the service strengths of the subset of the first plurality of grid executors.

11. The signal-bearing medium of claim 10, wherein the creating the training data further comprises:

selecting the plurality of types based on response time for the plurality of types at the subset of the first plurality of grid executors.

12. The signal-bearing medium of claim 9, wherein the selecting further comprises:

inputting a type of the second unit of work to the neural network; and

receiving a second service strength from the neural network.

13. The signal-bearing medium of claim 12, wherein the selecting further comprises:

selecting the second plurality of grid executors based on the second service strength from the neural network.

14. The signal-bearing medium of claim 8, wherein the receiving further comprises:

receiving services available from each of the first plurality of grid executors.

15. A method for configuring a computer, comprising:

configuring the computer to receive a service strength and services available from each of a first plurality of grid executors;

configuring the computer to select a subset of the first plurality of grid executors based on a priority and one of the service strength and services available;

configuring the computer to send a first plurality of units of work to the subset of the first plurality of grid executors in parallel;

configuring the computer to create training data based on performance of the subset of the first plurality of grid executors;

configuring the computer to train a neural network via the training data; and

configuring the computer to select a second plurality of grid executors via the neural network.

16. The method of claim 15, further comprising:

configuring the computer to send a second unit of work to the second plurality of grid executors in parallel.

17. The method of claim 15, wherein the configuring the computer to create the training data further comprises:

configuring the computer to create a plurality of pairs of input data and output data based on the performance, wherein the input data comprises a plurality of types of the first plurality of units of work and the output data comprises the service strengths of the subset of the first plurality of grid executors.

18. The method of claim 17, wherein the configuring the computer to create the training data further comprises:

configuring the computer to select the plurality of types based on response time for the plurality of types at the subset of the first plurality of grid executors.

19. The method of claim 16, wherein the configuring the computer to select further comprises:

configuring the computer to input a type of the second unit of work to the neural network; and

configuring the computer to receive a second service strength from the neural network.

20. The method of claim 19, wherein the configuring the computer to select further comprises:

configuring the computer to select the second plurality of grid executors based on the second service strength from the neural network.