SYSTEM AND METHOD FOR ROOT CAUSE ANALYSIS OF MOBILE NETWORK PERFORMANCE PROBLEMS

• US 20130262656A1
• Filed: 03/30/2012
• Published: 10/03/2013
• Est. Priority Date: 03/30/2012
• Status: Abandoned Application

• Current Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Original Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Inventors: Cao, Jin, Li, Li Erran, Bu, Tian, Sanders, Susan Wu
• Application Number:
  US13/436,212
• Publication Number:
  US 20130262656A1
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/5009 Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]

Abstract

A method and system for identifying causes of performance metric changes in a network by selecting, from a pool of network event counters, a plurality of candidate counters relevant to a performance metric; grouping the candidate counters into clusters of similar counters; selecting, from each cluster, one or more representative counters; and fitting the selected representative counters to a model of the performance metric to determine thereby a set of representative counters most relevant to the performance metric.

Claims

1. A method for identifying causes of performance metric changes in a network, the method comprising:

selecting, from a pool of network event counters, a plurality of candidate counters relevant to a performance metric;

grouping candidate counters into clusters of similar counters;

selecting, from each cluster, one or more representative counters; and

fitting the selected representative counters to a model of the performance metric to determine thereby a set of representative counters most relevant to the performance metric.

2. The method of claim 1, further comprising normalizing the values of said selected plurality of candidate counters.

3. The method of claim 1, wherein selecting the plurality of candidate counters comprises:

determining for each event counter a respective importance score for the performance metric; and

selecting as candidate counters for the performance metric those event counters having a respective importance score above a threshold level.

4. The method of claim 3, wherein said importance score is determined according to a rank correlation.

5. The method of claim 4, wherein said rank correlation comprises a Pearson correlation.

6. The method of claim 3, wherein said importance score is determined according to a Komogorov-Smirnov (KS) test.

7. The method of claim 1, wherein grouping the candidate counters into the clusters comprises:

computing a correlation between pairs of candidate counters to provide a plurality of nodes, wherein node edges are defined when an absolute value of a respective correlation exceeds a threshold level.

8. The method of claim 1, wherein said one or more representative counters comprises a single candidate counter having the largest correlation to the performance metric.

9. The method of claim 1, wherein said one or more representative counters comprises a predefined number of candidate counters having the largest correlation to the performance metric.

10. The method of claim 1, wherein said one or more representative counters comprises a set of candidate counters having a correlation to the performance metric above a threshold level.

11. The method of claim 1, wherein said fitting uses a regression analysis.

12. The method of claim 1, wherein said fitting uses a classification tree.

13. The method of claim 1, wherein said fitting uses a classification/regression tree adapted in accordance with a boosting procedure.

14. The method of claim 13, wherein said boosting procedure comprises an AdaBoost method.

15. The method of claim 1, wherein of said method is repeated for each of a plurality of performance metrics.

16. The method of claim 1, wherein said grouping is performed using one or more statistical clustering techniques.

17. The method of claim 16, wherein said statistical clustering techniques comprise any of a spectral clustering technique, a hierarchical clustering technique and a cost tree analysis technique.

18. An apparatus for use in a network management system and for identifying causes of performance metric changes in a network, the apparatus comprising:

a processor configured to;

select, from a pool of network event counters, a plurality of candidate counters relevant to a performance metric;

group candidate counters into clusters of similar counters;

select, from each cluster, one or more representative counters; and

fit the selected representative counters to a model of the performance metric to determine thereby a set of representative counters most relevant to the performance metric.

19. A tangible and non-transitory computer readable medium including software instructions which, when executed by a processer, perform a method for identifying causes of performance metric changes in a network, the method comprising:

selecting, from a pool of network event counters, a plurality of candidate counters relevant to a performance metric;

grouping candidate counters into clusters of similar counters;

selecting, from each cluster, one or more representative counters; and

fitting the selected representative counters to a model of the performance metric to determine thereby a set of representative counters most relevant to the performance metric.

20. A computer program product, wherein computer instructions, when executed by a processor in a computer, perform a method for identifying causes of performance metric changes in a network, the method comprising:

selecting, from a pool of network event counters, a plurality of candidate counters relevant to a performance metric;

grouping candidate counters into clusters of similar counters;

selecting, from each cluster, one or more representative counters; and

fitting the selected representative counters to a model of the performance metric to determine thereby a set of representative counters most relevant to the performance metric.