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Memory leak detection using transient workload detection and clustering

  • US 9,317,393 B2
  • Filed: 06/13/2013
  • Issued: 04/19/2016
  • Est. Priority Date: 06/13/2013
  • Status: Active Grant
First Claim
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1. A method, comprising:

  • performing, by one or more computing devices;

    classifying, based at least in part on a first statistical analysis, one or more performance metric collections corresponding to an observation period of an executing process into a first set of metrics representing one or more sub-periods during which a workload of the executing process exhibited a change trend, and a second set of metrics representing one or more sub-periods during which the workload did not exhibit a change trend;

    grouping, based at least in part on a second statistical analysis, the second set of metrics into one or more metric clusters, wherein each metric cluster of the one or more metric clusters represents a respective statistically stable workload level;

    detecting, based at least in part on a third statistical analysis of a particular performance metric corresponding to the one or more metric clusters, whether an upward trend in memory usage at the executing process during the observation time period is indicated; and

    in response to detecting that an upward trend in memory usage at the executing process during the observation time period is indicated, providing a notification of a potential memory leak at the executing process, wherein said notification of the potential memory leak comprises an indication of a confidence level associated with the upward trend in memory usage.

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