Capacity control

• US 9,176,789 B2
• Filed: 10/28/2009
• Issued: 11/03/2015
• Est. Priority Date: 10/31/2008
• Status: Expired due to Fees

• Current Assignee: HSBC Group Management Services Ltd. (HSBC Holdings plc)
• Original Assignee: HSBC Group Management Services Ltd. (HSBC Holdings plc)
• Inventors: Lowes, Raymond, Davies, Jonathan
• Primary Examiner(s): Mejia, Anthony
• Application Number:
  US13/126,627
• Publication Number:
  US 20110302301A1
• Time in Patent Office: 2,197 Days
• Field of Search: 709/200, 709/223, 709/224
• US Class Current: 1/1
• CPC Class Codes: G06F 2209/5019 Workload prediction
• CPC Class Codes: G06F 2209/508 Monitor
• CPC Class Codes: G06F 9/5083 Techniques for rebalancing the load in a distributed system

Abstract

There are described a system, apparatus and/or method according to which collected history data from monitorable and/or manageable entities within a server estate or data center is used to tailor a model to the past behavior and/or loading upon individual ones or parts of ones of the entities. The tailored model can then be used to predict a future value for one or more metrics included in the history data for an entity or part of an entity under consideration. The predicted value can then be used to determine whether some form of corrective action is required in respect of the entity, entity part or some other entity or entity part. Alternatively or in addition, predicted values for more than one entity or entity part can be aggregated to determine whether some form of corrective action is required in respect of the entity, entity part or some other entity or entity part. A corrective action can be applied on an automated or on a manual basis.

Claims

1. A computer implemented method for controlling a resource implemented by a computer system, the computer system providing a plurality of different services and the method comprising:

collecting operation data from each of a plurality of network resources comprising a plurality of computer processing resources, wherein different ones of the network resources operate under different management systems comprising at least one of different computer file management systems and different operating systems, and wherein the operation data is independent of the management system of the network resource to which the operation data relates;

predicting, by the computer system a future loading on at least one of the network resources using the operation data collected over time to provide an operation data history and model descriptors derived from a previous iteration, adjusting the model descriptors based upon latest data history, and storing the updated model descriptors for a future iteration;

determining by the computer system, using the predicted future loading, whether a corrective action is required;

determining by the computer system, when the corrective action is required, a predetermined corrective action for at least one of the plurality of network resources associated with first and second services of said plurality of services;

reassigning before a failure of the second service, by the computer system, at least one of the network resources from the first service of said plurality of services to the second service of said plurality of services responsive to priority levels associated with the first and second services.

2. The method of claim 1, wherein different ones of the plurality of network resources operate using different operating systems, and wherein the operation data is independent of the operating system of the network resource to which the operation data relates.

3. The method of claim 1, wherein the corrective action includes at least one action selected from the group comprising:

archival of data, compression of data, deletion of data, provision of additional resource, suppression of a service, and allocation of underused active resource to backup/idle.

4. The method of claim 3, wherein provision of additional resource comprises one or more of reallocation of resource from one service to a different service, and activation of at least one of a backup and idle resource.

5. The method of claim 3, wherein suppression of a service comprises selecting a lower priority service and suppressing availability of that service to enable resource to be reallocated to a higher priority service.

6. The method of claim 1, wherein the predicting uses a trending process to identify resource usage trends.

7. The method of claim 6, wherein the trending process uses a linear regression or time series analysis to identify resource usage trends.

8. The method of claim 7, wherein the time series analysis comprises double exponential smoothing or triple exponential smoothing.

9. The method of claim 1, wherein the predicting comprises using a mean squared difference to indicate an accuracy measure for the predicted future loading.

10. The method of claim 1, further comprising making at least one of the operation data history and the predicted future loading available to an administrator.

11. The method of claim 1, wherein the operation data history includes one data point for each measured metric to represent a given time period.

12. The method of claim 11, wherein the data point is selected from a plurality of values for the metric collected during the time period, or is an average value of a plurality of values for the metric during the time period.

13. The method of claim 1, wherein the operation data history includes data collected over a period of between 1 and 4 months.

14. The method of claim 1, wherein the determining also uses an accuracy measure for the predicted future loading to determine whether a corrective action can be applied based upon the predicted future loading.

15. The method of claim 1, wherein the priority level associated with the first service is lower than the priority level associated with the second service.

16. A computer architecture configured to control a resource, the computer architecture providing a plurality of different services and comprising:

at least one computer database storing historical data and configured to store an operation data history comprising operation data collected from each of a plurality of network resources comprising a plurality of computer resources, wherein different ones of the network resources operate under different management systems comprising at least one of different computer file management systems and different operating systems, and wherein the operation data is independent of the management system of the network resource to which the operation data relates;

a computer system in communication with the at least one computer database, wherein the computer system comprises a processor executing the following components;

a predictor configured to predict a future loading on at least one of the network resources by using the operation data in the operation data history and model descriptors derived from a previous iteration, adjusting the model descriptors based upon latest data history, and storing the updated model descriptors for a future iteration;

a determiner configured to determine, using the predicted future loading, whether a corrective action is required, and to determine, when the corrective action is required, a predetermined corrective action for at least one of the plurality of network resources associated with first and second services of said plurality of services;

an instruction generator configured to reassign, before a failure of the second service, at least one of the network resources from the first service of said plurality of services to the second service of said plurality of services responsive to priority levels associated with the first and second services.

17. A non-transitory computer-readable memory device comprising instructions, that when executed by a computer processor cause a programmable computer to execute the instructions that:

predict, by the computer system, a future loading on at least one of the network resources comprising a plurality of computer processing resources, wherein different ones of the network resources operate under different management systems comprising at least one of different computer file management systems and different operating systems, wherein the predicting includes using operation data collected over time to provide an operation data history and model descriptors derived from a previous iteration, adjusting the model descriptors based upon latest data history, and storing the updated model descriptors for a future iteration;

determine by the computer system, using the predicted future loading, whether a corrective action is required;

determine by the computer system, when the corrective action is required, a predetermined corrective action for at least one of the plurality of network resources associated with first and second services of said plurality of services;

reassign before a failure of the second service, by the computer system, at least one of the network resources from the first service of said plurality of services to the second service of said plurality of services responsive to priority levels associated with the first and second services.

18. A computer architecture configured to control a resource, the computer architecture providing a plurality of different services, comprising:

a plurality of network resources comprising a plurality of computer processing resources including a first network resource operating under a first management system comprising at least one of a first computer file management system and a first operating system, and a second network resource operating under a second management system different comprising at least one of a second computer file management system different than the first computer file management system and a second operating system different than the first operating system;

a data collection computer system collecting operation data from each of a plurality of network resources, wherein the operation data is independent of the management system of the network resource to which the operation data relates and includes operation data history;

at least one computer database in communication with and receiving the operation data collected by the data collection computer system, and storing the operation data collected from each of a plurality of network resources;

a computer system in communication with the at least one computer database, wherein the computer system comprises the following components executed by a processor of the computer system;

a predictor configured to predict, by the computer system, a future loading on at least one of the plurality of network resources by using the operation data and model descriptors from a previous iteration, adjusting the model descriptors based upon latest data history, and storing the updated model descriptors for a future iteration;

a determiner configured to determine, by the computer system, using the predicted future loading, whether a corrective action is required, and to determine, when the corrective action is required, a predetermined corrective action for at least one of the plurality of network resources associated with first and second services of said plurality of services;

an instruction generator configured, by the computer system, to generate an instruction to cause the predetermined corrective action to be applied to the network resource to reassign, before a failure of the second service, the at least one of the plurality of network resources from a first service of said plurality of services to a second service of said plurality of services, responsive to priority levels associated with said first and second services.