Forecasting capacity available for processing workloads in a networked computing environment

• US 8,806,003 B2
• Filed: 06/14/2011
• Issued: 08/12/2014
• Est. Priority Date: 06/14/2011
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Brown, Gene L., Coffey, Brendan F., Dawson, Christopher J., Harris, Clifford V., Koch, Lynn M.
• Primary Examiner(s): Pollack, Melvin H
• Application Number:
  US13/159,516
• Publication Number:
  US 20120324092A1
• Time in Patent Office: 1,155 Days
• Field of Search: 709/224
• US Class Current: 709/224
• CPC Class Codes: G06F 2209/5019 Workload prediction
• CPC Class Codes: G06F 2209/503 Resource availability
• CPC Class Codes: G06F 9/5044 considering hardware capabilities
• CPC Class Codes: G06F 9/5072 Grid computing
• CPC Class Codes: G06F 9/5083 Techniques for rebalancing the load in a distributed system
• CPC Class Codes: H04L 41/00 Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks

Abstract

Embodiments of the present invention provide an approach for forecasting a capacity available for processing a workload in a networked computing environment (e.g., a cloud computing environment). Specifically, aspects of the present invention provide service availability for cloud subscribers by forecasting the capacity available for running or scheduled applications in a networked computing environment. In one embodiment, capacity data may be collected and analyzed in real-time from a set of cloud service providers and/or peer cloud-based systems. In order to further increase forecast accuracy, historical data and forecast output may be post-processed. Data may be post-processed in a substantially continuous manner so as to assess the accuracy of previous forecasts. By factoring in actual capacity data collected after a forecast, and taking into account applications requirements as well as other factors, substantially continuous calibration of the algorithm can occur so as to improve the accuracy of future forecasts and enable functioning in a self-learning (e.g., heuristic) mode.

Claims

1. A computer-implemented method for forecasting an available capacity for processing a workload in a networked computing environment, comprising:

receiving and storing, in a computer data structure, capacity data corresponding to a set of peer systems in the networked computing environment;

accessing historical data for each of the set of peer systems, the historical data comprising information as to how the each of the set of peer systems handles workloads;

forecasting the available capacity of the set of peer systems to process the workload based on the capacity data and the historical data;

determining a set of consistency factors for each of the set of peer systems to prioritize the set of peer systems for processing the workload based on the historical data, each the set of consistency factors comprising a level of consistency with which at least one of the set of peer systems has historically processed workloads of an application type corresponding to the application type of the workload; and

prioritizing the set of peer systems for processing the workload based on the capacity and the set of consistency factors.

2. The computer-implemented method of claim 1, the networked computing environment comprising a cloud computing environment, and the set of peer systems comprising a set of cloud systems.

3. The computer-implemented method of claim 1, further comprising creating a data record based on the historical data, the data record being arranged according to a type of capacity for each of the peer systems based on the historical data, and the forecasting being further based on the data record.

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

comparing the capacity to a predefined service level;

determining if the predefined service level has been met; and

reallocating at least one resource in the networked computing environment responsive to the predefined service level not being met.

5. The computer-implemented method of claim 4, the predefined service level being derived from a service level agreement (SLA).

6. The computer-implemented method of claim 1, the receiving further comprising receiving input data comprising at least one of the following:

batch application data, executing application data, component level capacity data, application profiles, workload data, component threshold data, consumption pattern data, or risk model data.

7. The computer-implemented method of claim 1, the set of consistency factors indicating a historical level of consistency with which each of the set of peer systems has successfully processed workloads.

8. The computer-implemented method of claim 1, further comprising reprioritizing the set of peer systems based on at least one of the following:

updated capacity data, updated historical data, or an updated consistency factor.

9. A system for forecasting an available capacity for processing a workload in a networked computing environment, comprising:

a bus;

a processor coupled to the bus; and

a memory medium coupled to the bus, the memory medium comprising instructions to;

receive and store, in a computer data structure, capacity data corresponding to a set of peer systems in the networked computing environment;

access historical data for each of the set of peer systems, the historical data comprising information as to how the each of the set of peer systems handles workloads;

create a historical record according a type of capacity for each of the peer systems based on the historical data;

determine a set of consistency factors for each of the set of peer systems to prioritize the set of peer systems for processing the workload based on the historical data, each the set of consistency factors comprising a level of consistency with which at least one of the set of peer systems has historically processed workloads of an application type corresponding to the application type of the workload; and

prioritize the set of peer systems for processing the workload based on the capacity and the set of consistency factors.

10. The system of claim 9, the networked computing environment comprising a cloud computing environment, and the set of peer systems comprising a set of cloud systems.

11. The system of claim 9, the memory medium further comprising instructions to create a data record based on the historical data, the data record being arranged according to a type of capacity for each of the peer systems based on the historical data, and the forecasting being further based on the data record.

12. The system of claim 9, the memory medium further comprising instructions to:

compare the capacity to a predefined service level;

determine if the predefined service level has been met; and

reallocate at least one resource in the networked computing environment responsive to the predefined service level not being met.

13. The system of claim 12, the predefined service level being derived from a service level agreement (SLA).

14. The system of claim 9, the memory medium further comprising instructions to receive input data comprising at least one of the following:

batch application data, executing application data, component level capacity data, application profiles, workload data, component threshold data, consumption pattern data, or risk model data.

15. The system of claim 9, the set of consistency factors indicating a historical level of consistency with which each of the set of peer systems has successfully processed workloads.

16. The system of claim 9, the memory medium further comprising instructions to reprioritize the set of peer systems based on at least one of the following:

updated capacity data, updated historical data, or an updated consistency factor.

17. A computer program product for forecasting an available capacity for processing a workload in a networked computing environment, the computer program product comprising a computer readable storage media, and program instructions stored on the computer readable storage media, to:

receive and store, in a computer data structure, capacity data corresponding to a set of peer systems in the networked computing environment;

access historical data for each of the set of peer systems, the historical data comprising information as to how the each of the set of peer systems handles workloads;

create a historical record according a type of capacity for each of the peer systems based on the historical data;

determine a set of consistency factors for each of the set of peer systems to prioritize the set of peer systems for processing the workload based on the historical data, each the set of consistency factors comprising a level of consistency with which at least one of the set of peer systems has historically processed workloads of an application type corresponding to the application type of the workload; and

prioritize the set of peer systems for processing the workload based on the capacity and the set of consistency factors.

18. The computer program product of claim 17, the networked computing environment comprising a cloud computing environment, and the set of peer systems comprising a set of cloud systems.

19. The computer program product of claim 17, the computer readable storage media further comprising instructions to create a data record based on the historical data, the data record being arranged according to a type of capacity for each of the peer systems based on the historical data, and the forecasting being further based on the data record.

20. The computer program product of claim 17, the computer readable storage media further comprising instructions to:

compare the capacity to a predefined service level;

determine if the predefined service level has been met; and

reallocate at least one resource in the networked computing environment responsive to the predefined service level not being met.

21. The computer program product of claim 20, the predefined service level being derived from a service level agreement (SLA).

22. The computer program product of claim 17, the computer readable storage media further comprising instructions to receive input data comprising at least one of the following:

batch application data, executing application data, component level capacity data, application profiles, workload data, component threshold data, consumption pattern data, or risk model data.

23. The computer program product of claim 17, the set of consistency factors indicating a historical level of consistency with which each of the set of peer systems has successfully processed workloads.

24. The computer program product of claim 17, the computer readable storage media further comprising instructions to reprioritize the set of peer systems based on at least one of the following:

updated capacity data, updated historical data, or an updated consistency factor.

25. A method for deploying a computer infrastructure for forecasting an available capacity for processing a workload in a networked computing environment, comprising:

providing a computer infrastructure being operable to;

receive and store, in a computer data structure, capacity data corresponding to a set of peer systems in the networked computing environment;

access historical data for each of the set of peer systems, the historical data comprising information as to how the each of the set of peer systems handles workloads;

forecast the available capacity of the set of peer systems to process the workload based on the capacity data and the historical data;

determine a set of consistency factors for each of the set of peer systems to prioritize the set of peer systems for processing the workload based on the historical data, each the set of consistency factors comprising a level of consistency with which at least one of the set of peer systems has historically processed workloads of an application type corresponding to the application type of the workload; and

prioritize the set of peer systems for processing the workload based on the capacity and the set of consistency factors.