Self-tuning statistical resource leak detection

Patent metadata

Field	Value
Publication number	US-9104563-B2
Application number	US-201213370148-A
Country	US
Kind code	B2
Filing date	Feb 9, 2012
Priority date	Feb 9, 2012
Publication date	Aug 11, 2015
Grant date	Aug 11, 2015

Abstract

Self-tuned detection of memory leaks or other resource leaks is described. Sample size and sample rate are set manually or computationally selected. Self-tuning leak detection code uses one or more self-tuning mechanisms to exclude outlier sample points, to perform a second order linear regression, and/or to identify a derivative of a sequence of linear regression slopes. Statistical analysis computationally proactively determines what trend is present: upward, steady, or downward. Analysis may compare a linear regression slope to a threshold at which the slope realizes an upward trend, possibly only after crossing the threshold a specified number of times. Regression calculation may be optimized by setting an origin to the median of the time values and setting a scale to their constant time interval. A watchdog may use self-tuned detection to monitor processes, for efficiently recycling processes to prevent problems caused by resource loss.

First claim

What is claimed is:

1. A computational proactive process for self-tuned memory leak detection, comprising the steps of:
noting multiple memory usage sample points which show memory usage for a computational service, each noted sample point including a sample time and a memory size, the noted sample points having a mean;
tuning at least some of the noted sample points for memory leak detection by computationally proactively doing at least one of the following: (a) double smoothin…

Assignees

• Taskov Alexander
• Microsoft Technology Licensing Llc

Inventors

• Taskov Alexander

Classifications

• G06F11/366

  Physics · mapped topic

• G06F11/073 Primary

  Physics · mapped topic

• G06F11/3452

  Physics · mapped topic

• G06F11/0754

  Physics · mapped topic