Automatic capture of detailed analysis information for web application outliers with very low overhead

• US 9,311,598 B1
• Filed: 02/02/2012
• Issued: 04/12/2016
• Est. Priority Date: 02/02/2012
• Status: Active Grant

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: AppDynamics, Inc. (Cisco Systems, Inc.)
• Inventors: Bansal, Jyoti, Sankara, Bhaskar, Acharya, Manoj, Srinivasaiah, Vinay, Thomas, Binil
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Zidanic, Michael
• Application Number:
  US13/365,171
• Time in Patent Office: 1,531 Days
• Field of Search: 706/12
• US Class Current: 1/1
• CPC Class Codes: G06F 11/3003 Monitoring arrangements specially adapted to the computing system or computing system component being monitored
• CPC Class Codes: G06F 11/302 where the computing system component is a software system
• CPC Class Codes: G06F 11/3093 Configuration details thereof, e.g. installation, enabling, spatial arrangement of the probes
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3419 by assessing time
• CPC Class Codes: G06F 11/3466 Performance evaluation by tracing or monitoring
• CPC Class Codes: G06F 2201/81 Threshold
• CPC Class Codes: G06F 2201/815 Virtual middleware or OS functionality using virtual machines to implement generic software techniques for error detection or fault masking G06F11/1484
• CPC Class Codes: G06F 2201/865 Monitoring of software
• CPC Class Codes: G06F 2201/87 Monitoring of transactions
• CPC Class Codes: G06F 2201/875 Monitoring of systems including the internet
• CPC Class Codes: G06F 9/45533 Hypervisors; Virtual machine monitors
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/00 Computing arrangements using knowledge-based models
• CPC Class Codes: G06Q 20/00 Payment architectures, schemes or protocols apparatus for performing or posting payment transactions G07F7/08, G07F19/00; electronic cash registers G07G1/12
• CPC Class Codes: H04L 43/022 by sampling
• CPC Class Codes: H04L 43/045 for graphical visualisation of monitoring data
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

A system monitors a network or web application provided by one or more distributed applications and provides data for each and every method instance in an efficient low-cost manner. The web application may be provided by one or more web services each implemented as a virtual machine or one or more applications implemented on a virtual machine. Agents may be installed on one or more servers at an application level, virtual machine level, or other level. The agent may identify one or more hot spot methods based on current or past performance, functionality, content, or business relevancy. Based on learning techniques, efficient monitoring, and resource management, the present system may capture data for and provide analysis information for outliers of a web application with very low overhead.

Claims

1. A method for monitoring a business transaction, comprising:

recording performance data for each of a plurality of methods of a network application, the performance data stored by an agent in a header of an execution thread that executes the method, the agent stored on a machine that executes the plurality of methods;

ignoring the performance data for one or more of the plurality of methods that satisfy a threshold;

generating a call graph from the performance data, the call graph indicating the root method of the business transaction and each method called as part of completion of the business transaction; and

storing the performance data for one or more of the plurality of methods having a root node that does not satisfy a condition.

2. The method of claim 1, wherein the performance data includes a start time and an end time for execution of the method.

3. The method of claim 1, wherein the condition includes a threshold for time of execution.

4. The method of claim 1, wherein the condition includes calling an external Java Virtual Machine.

5. The method of claim 1, wherein storing the data includes storing execution data and external call data.

6. The method of claim 1, wherein the performance data is stored in an array within the execution thread header.

7. The method of claim 1, further comprising:

building a call graph for a Java Virtual Machine; and

identifying methods within the call graph that do not satisfy the condition.

8. The method of claim 1, further comprising:

qualifying the methods associated with the stored data;

storing a portion of the stored data based on the qualification of the methods.

9. The method of claim 1, further comprising processing a stored hot spot data to generate a graphical representation of the methods.

10. The method of claim 1, further comprising:

automatically identifying one of the plurality of methods executed as part of a business transaction as part of a learning process; and

recording performance data for each of a plurality of methods associated with the business transaction.

11. The method of claim 10, wherein automatically identifying includes pre-loading previously stored methods associated with the business transaction.

12. The method of claim 11, further comprising ending the learning process based on a number of times that automatically identifying is performed.

13. The method of claim 12, further comprising re-starting the learning process after a period of time.

14. A non-transitory computer-readable storage medium having embodied thereon a program, the program being executable by a processor to perform a method for monitoring a transaction, the method comprising

recording performance data for each of a plurality of methods of a network application, the performance data stored by an agent in a header of an execution thread that executes the method, the agent stored on a machine that executes the plurality of methods;

ignoring the performance data for one or more of the plurality of methods that satisfy a threshold;

generating a call graph from the performance data, the call graph indicating the root method of the business transaction and each method called as part of completion of the business transaction; and

storing the performance data for one or more of the plurality of methods having a root node that does not satisfy a condition.

15. The non-transitory computer-readable storage medium of claim 14, wherein the performance data includes a start time and an end time for execution of the method.

16. The non-transitory computer-readable storage medium of claim 14, wherein the condition includes a threshold for time of execution.

17. The non-transitory computer-readable storage medium of claim 14, wherein the condition includes calling an external Java Virtual Machine.

18. The non-transitory computer-readable storage medium of claim 14, wherein storing the data includes storing execution data and external call data.

19. The non-transitory computer-readable storage medium of claim 14, wherein the performance data is stored in an array within the execution thread header.

20. The non-transitory computer-readable storage medium of claim 14, the method further comprising:

building a call graph for a Java Virtual Machine; and

identifying methods within the call graph that do not satisfy the condition.

21. The non-transitory computer-readable storage medium of claim 14, the method further comprising:

qualifying the methods associated with the stored data;

storing a portion of the stored data based on the qualification of the methods.

22. The non-transitory computer-readable storage medium of claim 14, the method further comprising processing a stored hot spot data to generate a graphical representation of the methods.

23. The non-transitory computer-readable storage medium of claim 14, the method further comprising:

automatically identifying one of the plurality of methods executed as part of a business transaction as part of a learning process; and

recording performance data for each of a plurality of methods associated with the business transaction.

24. The non-transitory computer-readable storage medium of claim 23, wherein automatically identifying includes pre-loading previously stored methods associated with the business transaction.

25. The non-transitory computer-readable storage medium of claim 24, the method further comprising ending the learning process based on a number of times that automatically identifying is performed.

26. The non-transitory computer-readable storage medium of claim 25, the method further comprising re-starting the learning process after a period of time.