COMPUTER PERFORMANCE PREDICTION USING SEARCH TECHNOLOGIES

• US 20150205690A1
• Filed: 01/13/2015
• Published: 07/23/2015
• Est. Priority Date: 01/23/2014
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Concurix Corporation
• Inventors: SETO, Tetsuo
• Granted Patent: US 10,452,458 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: G06F 11/323 Visualisation of programs or trace data
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3442 for planning or managing the needed capacity
• CPC Class Codes: G06F 11/3447 Performance evaluation by modeling
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3466 Performance evaluation by tracing or monitoring
• CPC Class Codes: G06F 2201/86 Event-based monitoring

Abstract

A computer monitoring system may predict near term and long term performance by comparing a segment of current time series data with previously observed time series to find matching segments. From a matching segment, a prediction of performance may be made by examining later observations in the time series. Each time series element may include a large number of parameters, and one mechanism for comparing segments may be treating the elements as multi-dimensional vectors and using cosine similarity for finding significant matches. A deployment mechanism may store time series segments in a searchable database, and search the database with a newly observed time series segment for matches.

Claims

1. A method performed on at least one computer processor, said method comprising:

receiving a first time series segment, said first time series segment comprising performance data observed while executing a first computer application;

identifying a second time series segment from a historical database, said historical database comprising time series observed while executing said first computer application, said second time series segment being similar to said first time series segment;

identifying a third time series segment from said historical database, said third time series being later in sequence from said second time series segment; and

creating a prediction based on said third time series segment.

2. The method of claim 1 further comprising:

identifying a fourth time series segment from said historical database, said fourth time series segment being similar to said first time series segment;

identifying a fifth time series segment from said historical database, said fifth time series being later in sequence from said fourth time series segment; and

creating said prediction based on said third time series segment and said fifth time segment.

3. The method of claim 2, said prediction being created by at least weighting said third time series with a first weight and weighting said fifth time series with a second weight.

4. The method of claim 2, said historical database comprising a plurality of time series, at least one of said time series being observations while executing said first computer application on a first execution platform and at least a second one of said time series being observations while executing said first computer application on a second execution platform.

5. The method of claim 4, said third time series segment being observations from said first execution platform and said fifth time series segment being observations from said second execution platform.

6. The method of claim 4, said historical database further comprising at least one time series being observations while executing a second computer application.

7. The method of claim 6, said third time series segment being observations from said first computer application and said fifth time series segment being observations from said second computer application.

8. The method of claim 7, said first computer application being a version of said second computer application.

9. The method of claim 1, said performance data comprising performance data for a first function and a second function within said first application.

10. The method of claim 9, said performance data comprising aggregated performance data for a time interval in said time series.

11. A system comprising:

a processor;

a database comprising a plurality of time series representing performance observations from tracing a first application;

a search engine that;

receives a first time series segment, said first time series segment comprising performance data observed while executing said first computer application;

identifies a second time series segment from said historical database, said second time series segment being similar to said first time series segment; and

returns said second time series segment.

an analysis engine that;

identifies a third time series segment from said historical database, said third time series being later in sequence from said second time series segment; and

creating a prediction based on said third time series segment.

12. The system of claim 11, said search engine that further:

identifies a fourth time series segment from said historical database, said fourth time series segment being similar to said first time series segment;

said analysis engine that further;

identifies a fifth time series segment from said historical database, said fifth time series being later in sequence from said fourth time series segment; and

creates said prediction based on said third time series segment and said fifth time segment.

13. The system of claim 12, said prediction being created by at least weighting said third time series with a first weight and weighting said fifth time series with a second weight.

14. The system of claim 12, said historical database comprising a plurality of time series, at least one of said time series being observations while executing said first computer application on a first execution platform and at least a second one of said time series being observations while executing said first computer application on a second execution platform.

15. The system of claim 14, said third time series segment being observations from said first execution platform and said fifth time series segment being observations from said second execution platform.

16. The system of claim 14, said historical database further comprising at least one time series being observations while executing a second computer application.

17. The system of claim 16, said third time series segment being observations from said first computer application and said fifth time series segment being observations from said second computer application.

18. The system of claim 17, said first computer application being a version of said second computer application.

19. The system of claim 11, said performance data comprising performance data for a first function and a second function within said first application.

20. The system of claim 19, said performance data comprising aggregated performance data for a time interval in said time series.