ANALYSIS OF ENERGY-RELATED FACTORS FOR SELECTING COMPUTATIONAL JOB LOCATIONS

• US 20100057641A1
• Filed: 09/03/2008
• Published: 03/04/2010
• Est. Priority Date: 09/03/2008
• Status: Active Grant

• Current Assignee: Kyndryl Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Jackson, Steven V., Boss, Gregory J., Hamilton, Rick A. II, Sand, Anne R., Doran, James R.
• Granted Patent: US 8,086,544 B2
• Time in Patent Office: Days
• US Class Current: 705/412
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: Y02P 90/82 Energy audits or management systems therefor

Abstract

The selection of an optimal data center location for running a computational workload is based on multiple energy criteria. The location is chosen based on multivariate and predictive analysis of total direct and indirect energy costs, and other user-defined factors. Among the direct and indirect costs are power costs and cooling costs as well as structural and other details of a given data center. Among the other factors to be considered that can have an impact on present and future costs are weather patterns, data and forecasts, availability of energy providers, and energy attributes. A forecaster factors these direct and indirect costs along with extrinsic information such as historical trends and predictive sources into a forecast which is then input to a decision engine along with user defined criteria and with anticipated compute tasks and requirements to select a final location or locations for handling the workload.

Claims

1. A system for determining a physical location for running a computational workload, comprising:

a data collector for collecting multivariable energy-related data from diverse sources;

a forecasting module for receiving the collected energy-related data and other criteria affecting overall computing costs, for predicting projected energy consumption based on the collected data, and generating an output; and

a rules engine to compare the output of the forecasting module with predefined criteria, and to select a location based on energy costs and other criteria.

2. The system according to claim 1 wherein the diverse sources of multivariable energy-related data includes energy providers, power costs, weather data, energy attributes, and cooling costs.

3. The system according to claim 1 wherein projected energy consumption is based on the collected data, historical trends and predictive sources.

4. The system according to claim 1 wherein the rules engine utilizes information based on compute tasks, requirements of the computational workload, data from the data collector and the output of the forecasting module to make the location selection.

5. The system according to claim 4 wherein the rules engine also uses non-power factors including computer processing speed, data center charges, environmental and business risks, labor rates and processing overhead to assist in making the location selection.

6. A method for determining a physical location for running a computational workload comprising:

collecting multivariable energy-related data from diverse sources;

predicting projected energy consumption based on the collected data and on other criteria affecting overall costs;

comparing an output of a forecasting module with predefined criteria; and

selecting a location based on the energy costs and other cost-effecting criteria.

7. The method according to claim 6 wherein the diverse sources of multivariable energy-related data include power costs, weather data, energy attributes, energy providers, and cooling costs.

8. The method according to claim 6 wherein the projection of energy consumption is based on the collected data, historical trends and predictive sources.

9. The method according to claim 6 wherein the selection utilizes information based on compute tasks and requirements of the computational workload, data from the data collector and the output of the forecasting module to make the location selection.

10. The method according to claim 9 wherein the selection also relies on other criteria including non-power factors comprising computer processing speed, data center charges, environmental and business risks, labor rates and processing overhead.

11. A computer-readable medium containing instructions, when implemented on a computer, for determining a physical location for running a computational workload by a method comprising:

collecting multivariable energy-related data from diverse sources;

predicting projected energy consumption based on the collected data and on other criteria affecting overall costs;

comparing the output of a forecasting module with predefined criteria; and

selecting a location based on the energy costs and other cost-effecting criteria.

12. The computer-readable medium containing instructions according to claim 11 wherein the diverse sources of multivariable energy-related data includes energy providers, power costs, weather data, energy attributes, and cooling costs.

13. The computer-readable medium containing instructions according to claim 11 wherein projected energy consumption is based on the collected data, historical trends and predictive sources.

14. The computer-readable medium containing instructions according to claim 11 wherein the rules engine utilizes information based on the compute tasks and requirements of the computational workload as well as the collected multivariable data and the output of the forecasting module to make the location selection.

15. The computer readable medium containing instructions according to claim 11 wherein the other criteria includes non-power factors including computer processing speed, data center charges, environmental and business risks, labor rates and processing overhead.

16. A computer product including a medium on which a computer program is recorded, said computer program including:

a first set of instructions for collecting multivariable energy-related data from diverse sources;

a second set of instructions for predicting projected energy consumption based upon receipt of the collected data;

a third set of instructions for a decision engine to compare the output of the forecasting module with predefined criteria, and to select a location based on energy costs and other criteria; and

program logic for initiating said sets of instructions to make a determination regarding the selection of a location.

17. The computer product according to claim 16 wherein the computer program includes instructions for utilizing information based on compute tasks and requirements of the computational workload, data from the collected data and the projected energy consumption prediction to make the location selection.

18. The computer product according to claim 17 wherein the computer program also includes instructions for basing the selection on other criteria including non-power factors comprising computer processing speed, data center charges, environmental and business risks, labor rates and processing overhead.

19. A method for deploying an application for selecting a physical location for running a compute workload, comprising the steps of:

collecting multivariable energy-related data from diverse sources;

predicting projected energy consumption based on the collected data and on other criteria affecting overall costs;

comparing the output of a forecasting module with predefined criteria; and

selecting a location based on the energy costs and other cost-effecting criteria.

20. The method according to claim 19 wherein the diverse sources of multivariable energy-related data include power costs, weather data, energy attributes and cooling costs.

21. The method according to claim 19 wherein the projection of energy consumption is based on the collected data, historical trends and predictive sources.

22. The method according to claim 19 wherein a decision engine utilizes information based on compute tasks and requirements of the computational workload, data from the data collector and the output of the forecasting module to make the location selection.

23. The method according to claim 19 wherein the other criteria includes non-power factors including computer processing speed, data center charges, environmental and business risks, labor rates and processing overhead.