VISUALIZATION OF BEHAVIOR CLUSTERING OF COMPUTER APPLICATIONS

• US 20150205693A1
• 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 9,870,294 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/3065 Monitoring arrangements determined by the means or processing involved in reporting the monitored data error or fault reporting or logging G06F11/0766
• 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 11/3452 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

Dimensionality reduction, such as principal component analysis, may be performed against a time series of performance observations for a computer application. A visual representation of the results may be displayed in one, two, or three dimensions, and often show clusters of operational behavior. The representation may be animated to show a sequence of observations and how the behavior of an application may change from one cluster of operation to another. The representation may be further applied to show both a historical view of the observations and new observations. The time series may contain performance and operational data, as well as metadata observed from a computer application.

Claims

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

generating a dimensionality reduction transformation representing a first time series collected from tracing a computer application, said first time series comprising performance data for a plurality of functions at each observation of said first time series, each of said plurality of functions being part of said computer application;

receiving a second time series collected from tracing said computer application and applying said dimensionality reduction transformation to generate a transformed dataset; and

causing said transformed dataset to be displayed.

2. The method of claim 1, said performance observations comprising resource utilization for each of said plurality of functions.

3. The method of claim 2, said resource utilization being at least one of a group composed of:

memory utilization;

processor utilization; and

network utilization.

4. The method of claim 1, said transformed dataset being displayed in two dimensions.

5. The method of claim 1, said transformed dataset being displayed in three dimensions.

6. The method of claim 1, said transformed dataset being displayed using animation showing a sequence of said observations in said transformed dataset.

7. The method of claim 6, said transformed dataset being displayed in at least near-real time.

8. The method of claim 1, at least some of said functions being part of a library of functions.

9. The method of claim 1 further comprising:

receiving a predicted observation, said predicted observation being predicted based on said second time series;

applying said dimensionality reduction transformation to generate a transformed predicated observation; and

causing said transformed predicted observation to be displayed.

10. A system comprising:

at least one computer processor;

a dimensionality reduction analyzer executing on said at least one computer processor, said dimensionality reduction analyzer that;

generates a dimensionality reduction transformation representing a first time series collected from tracing a computer application, said first time series comprising performance data for a plurality of functions at each observation of said first time series, each of said plurality of functions being part of said computer application;

a display engine that;

receives a second time series collected from tracing said computer application and applies said dimensionality reduction transformation to generate a transformed dataset; and

causes said transformed dataset to be displayed.

11. The system of claim 10, said performance observations comprising resource utilization for each of said plurality of functions.

12. The system of claim 11, said resource utilization being at least one of a group composed of:

memory utilization;

processor utilization; and

network utilization.

13. The system of claim 10, said transformed dataset being displayed in two dimensions.

14. The system of claim 10, said transformed dataset being displayed in three dimensions.

15. The system of claim 10, said transformed dataset being displayed using animation showing a sequence of said observations in said transformed dataset.

16. The system of claim 15, said transformed dataset being displayed in at least near-real time.

17. The system of claim 10, at least some of said functions being part of a library of functions.