ADAPTIVE UPGRADE TO COMPUTING SYSTEMS

• US 20160274893A1
• Filed: 02/24/2016
• Published: 09/22/2016
• Est. Priority Date: 03/16/2015
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Kaminski, Tomasz K., Thomas, Satish J., Jewsbury, Andrew William
• Granted Patent: US 9,886,262 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 8/61 Installation
• CPC Class Codes: G06F 8/65 Updates security arrangements therefor G06F21/57
• CPC Class Codes: G06F 8/71 Version control security arrangements therefor G06F21/57; Configuration management
• CPC Class Codes: G06F 9/44505 Configuring for program initiating, e.g. using registry, configuration files
• CPC Class Codes: G06F 9/44536 Selecting among different versions

Abstract

An upgrade or update that is available to a deployed computing system (or deployment) under analysis is identified. Performance improvement metrics measured at similar deployments, that incorporated the identified update, are accessed to estimate a performance improvement metric corresponding to incorporating the identified update at the deployment under analysis.

Claims

1. A computing system, comprising:

a process modeling component configured to identify a set of deployed processes on a computing system under analysis;

an applicable upgrade identification system configured to identify an applicable upgrade available to the computing system under analysis, based on the identified set of deployed processes;

a machine learning system configured to access a measured performance metric corresponding to another computing system that incorporated the applicable upgrade and to generate a performance change metric indicative of an estimated change in a performance metric for the computing system under analysis, based on incorporation of the applicable upgrade into the computing system under analysis; and

a recommendation engine that generates an upgrade recommendation that includes the applicable upgrade and the performance change metric.

2. The computing system of claim 1 wherein the applicable upgrade identification system comprises:

upgrade identifier logic that identifies, as applicable upgrades, available upgrades to the deployed processes in the set of deployed processes.

3. The computing system of claim 2 wherein the applicable upgrade identification system comprises:

weight generation logic that generates an impact weight indicative of an impact of each of the applicable upgrades on the deployed processes.

4. The computing system of claim 3 wherein the process modeling component is further configured to identify usage patterns corresponding to the set of deployed processes indicative of usage of the set of deployed processes in the computing system under analysis, the weight generation logic generating the impact weight as having more impact if a given available upgrade affects a deployed process that is heavily used in the computing system under analysis, based on the identified usage patterns.

5. The computing system of claim 4 wherein the machine learning system comprises:

an upgrade effort estimation component configured to access a measured effort metric corresponding to another computing system that incorporated the applicable upgrade, the measured effort metric being indicative of effort used in incorporating the applicable upgrade into the other computing system, and to generate an upgrade effort metric indicative of an estimated, measurable effort for incorporating the applicable upgrade into the computing system under analysis.

6. The computing system of claim 5 wherein the recommendation engine comprises:

recommended upgrade identifier logic that compares the upgrade effort metric to the performance change metric to identify a recommended upgrade to be included in the upgrade recommendation.

7. The computing system of claim 6 wherein the machine learning system is configured to obtain an upgrade criterion and generate the performance change metric relative to the upgrade criterion.

8. The computing system of claim 7 wherein the process modeling component is configured to identify customizations to the deployed processes in the set of deployed processes in the computing system under analysis.

9. The computing system of claim 8 wherein the machine learning system Is configured to identify, as the other computing system, a similar computing system that is similar to the computing system under analysis based on the set of deployed processes in the computing system under analysis and the other computing system, the usage patterns in the computing system under analysis and the other computing system, the customizations to the set of deployed processes in the computing system under analysis and the other computing system, and the upgrade criterion for the computing system under analysis and the other computing system.

10. The computing system of claim 9 wherein the recommendation engine comprises:

sequence identifier logic configured to, when the upgrade recommendation identifies a plurality of recommended upgrades, identify a recommended upgrade sequence for incorporating the plurality of recommended upgrades into the computing system under analysis.

11. The computing system of claim 10 and further comprising:

an upgrade component configured to access an upgrade system and automatically provision a test environment and deploy the recommended upgrades into the test environment and to measure the performance change metric based on incorporation of the recommended upgrades to the computing system under analysis in the test environment, the recommendation engine updating the recommendation, based on the measured performance change metric, measured in the test environment.

12. The computing system of claim 11 wherein the upgrade component is further configured to access the upgrade system and perform the recommended upgrades in a production environment for the computing system under analysis, measure the effort metric corresponding to performing the recommended upgrades in the production environment and to measure the change in the performance metric based on performing the recommended upgrades in the production environment, and feed the measured effort metric corresponding to performing the recommended upgrades in the production environment and the measured change in the performance metric, back to the machine learning system.

13. A computer implemented method, comprising:

identifying a set of deployed processes on a computing system under analysis;

comparing the set of deployed processes to available upgrades to identify an applicable upgrade available to the computing system under analysis;

obtaining a measured performance metric corresponding to another computing system that incorporated the applicable upgrade;

generating a performance change metric indicative of an estimated change in a performance metric for the computing system under analysis, corresponding to incorporation of the applicable upgrade into the computing system under analysis, based on the measured performance metric obtained for the other computing system; and

a recommendation engine that generates an upgrade recommendation that includes the applicable upgrade and the performance change metric.

14. The computer implemented method of claim 13 and further comprising:

generating an impact weight indicative of an impact of the applicable upgrade on the deployed processes.

15. The computer implemented method of claim 14 wherein identifying a set of deployed processes further comprises:

identifying usage patterns corresponding to the set of deployed processes indicative of usage of the set of deployed processes in the computing system under analysis, wherein generating an impact weight comprises generating the impact weight as having more impact if a given available upgrade affects a deployed process that is heavily used in the computing system under analysis, based on the identified usage patterns.

16. The computer implemented method of claim 15 and further comprising:

obtaining a measured effort metric corresponding to the other computing system that incorporated the applicable upgrade, the measured effort metric being indicative of effort used in incorporating the applicable upgrade into the other computing system; and

generating an upgrade effort metric indicative of an estimated, measurable effort for incorporating the applicable upgrade into the computing system under analysis.

17. The computer implemented method of claim 16 wherein identifying a set of deployed processes comprises:

identifying customizations to the deployed processes in the set of deployed processes in the computing system under analysis.

18. The computer implemented method of claim 17 wherein obtaining a measured performance metric comprises:

identifying, as the other computing system, a similar computing system that is similar to the computing system under analysis based on the set of deployed processes in the computing system under analysis and the other computing system, the usage patterns in the computing system under analysis and the other computing system, and the customizations to the set of deployed processes in the computing system under analysis and the other computing system.

19. A computing system, comprising:

a process modeling component configured to identify a set of deployed processes on a computing system under analysis, usage patterns corresponding to the set of deployed processes, indicative of usage of the set of deployed processes in the computing system under analysis, and customizations to the deployed processes in the set of deployed processes in the computing system under analysis;

an applicable upgrade identification system configured to identify an applicable upgrade available to the computing system under analysis, based on the identified set of deployed processes;

a machine learning system configured to access a measured performance metric and a measured effort metric corresponding to another computing system that incorporated the applicable upgrade and to generate a performance change metric indicative of an estimated change in a performance metric for the computing system under analysis, based the measured performance metric, and to generate an upgrade effort metric indicative of an estimated, measurable effort for incorporating the applicable upgrade into the computing system under analysis, based on the measured effort metric; and

a recommendation engine that generates an upgrade recommendation that includes the applicable upgrade and the performance change metric.

20. The computing system of claim 19 wherein the machine learning system is configured to identify, as the other computing system, a similar computing system that is similar to the computing system under analysis based on the set of deployed processes in the computing system under analysis and the other computing system, the usage patterns in the computing system under analysis and the other computing system, and the customizations to the set of deployed processes in the computing system under analysis and the other computing system.