PERFORMANCE ANALYSIS OF APPLICATIONS

• US 20110276836A1
• Filed: 10/16/2008
• Published: 11/10/2011
• Est. Priority Date: 10/16/2008
• Status: Active Grant

• Current Assignee: Micro Focus LLC (Open Text Corporation)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Bernstein, Ruth, Kahana, Chen, Gavish, Ifat
• Granted Patent: US 8,635,498 B2
• Time in Patent Office: Days
• US Class Current: 714/38.1
• CPC Class Codes: G06F 11/0709 in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3438 monitoring of user actions tracking the activity of the user H04L67/535
• CPC Class Codes: G06F 11/3447 Performance evaluation by modeling
• CPC Class Codes: G06F 11/3476 Data logging G06F11/14, G06F11/2205 take precedence
• CPC Class Codes: G06F 2201/80 Database-specific techniques
• CPC Class Codes: G06F 2201/81 Threshold
• CPC Class Codes: G06F 2201/86 Event-based monitoring
• CPC Class Codes: G06F 2201/865 Monitoring of software
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202

Abstract

Embodiments of methods and systems for analyzing performance of an application are provided. In that regard, an embodiment of a method for analyzing performance, among others, comprises collecting performance metric data from the application over time; segmenting the performance metric data into time segments representing sets of contiguous time samples which exhibit similar performance metric behaviour; determining the presence of an anomaly in a time segment; and correlating the anomalous segment with other data available to the system to determine the cause of the anomaly.

Claims

1. A method for analyzing performance of an application, comprising:

collecting performance metric data from the application over time;

segmenting the performance metric data into time segments representing sets of contiguous time samples which exhibit similar performance metric behaviour;

determining the presence of an anomaly in a time segment; and

correlating the anomalous segment with other data available to the system to determine the cause of the anomaly.

2. The method of claim 1, wherein the performance metric data comprises at least one of response time, number of unavailabilities per unit of time, throughput, and hits per second.

3. The method of claim 1, wherein determining the presence of an anomaly comprises comparing a baseline state of the application with the performance metric data from a time segment to determine the presence of an anomaly in the time segment.

4. The method of claim 1, wherein determining the presence of an anomaly comprises comparing the performance metric data from a time segment with performance metric data from a past time segment to determine the presence of an anomaly in the time segment.

5. The method of claim 1, further comprising querying a database regarding changes to the application or changes to a supporting component of the application that occurred during a time segment.

6. The method of claim 1, further comprising querying a database to identify a support component of the application upon detection of an anomaly in the performance metric data from a time segment.

7. The method of claim 6, wherein the other data available to the system for correlation includes a set of performance metric data of the support component from the time segment.

8. The method of claim 1, further comprising performing a commonality analysis between a detected anomalous segment and past anomalous segments.

9. The method of claim 1, further comprising identifying a pattern in the collected performance metric data, and classifying the anomaly into a category based on the identified pattern.

10. The method of claim 9, further comprising projecting trends in the identified pattern to identify future anomalies.

11. A computer readable storage medium comprising a program for analyzing performance of an application, the program when executed by a computer processor causing the processor to perform the steps of:

collecting performance metric data from the application;

segmenting the performance metric data into time segments;

determining the presence of an anomaly in a time segment; and

correlating the performance metric data to determine the cause of the anomaly.

12. A system for analyzing performance of an application, comprising:

an analysis module configured to collect performance metric data from the application, the analysis module further configured to identify an anomaly in one or more of time segments of the collected performance metric data;

a segmentation engine configured to segment the collected performance metric data into time segments; and

a correlation engine configured to determine a cause of the identified anomaly.