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A Framework for Detecting System Performance Anomalies Using Tracing Data Analysis.

Iman Kohyarnejadfard1, Daniel Aloise1, Michel R Dagenais1

  • 1Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada.

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|August 27, 2021
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Summary
This summary is machine-generated.

This study introduces an anomaly detection framework to quickly identify software performance issues. It uses machine learning on system call data to pinpoint performance anomalies, reducing troubleshooting time for developers.

Keywords:
anomaly detectionmachine learningoperating systemperformance evaluationtracing

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Area of Science:

  • Computer Science
  • Software Engineering
  • Machine Learning

Background:

  • Complex software architectures are susceptible to performance anomalies from bugs, hardware issues, or resource contention.
  • Traditional performance metrics offer limited insight into the root causes of abnormal system behavior.
  • System experts often face challenges sifting through extensive low-level trace data to diagnose performance problems.

Purpose of the Study:

  • To develop an anomaly detection framework to reduce software performance troubleshooting time.
  • To guide developers in identifying performance bottlenecks by highlighting anomalous trace data.
  • To improve the efficiency of diagnosing performance issues in large-scale software systems.

Main Methods:

  • Collecting system call streams during process execution using Linux Trace Toolkit Next Generation (LTTng).
  • Employing a machine learning module to analyze system call execution times and frequency.
  • Identifying anomalous subsequences within the collected trace data.

Main Results:

  • The proposed framework effectively distinguishes normal system call sequences from abnormal ones.
  • Experiments on real-world datasets (MySQL, Chrome) demonstrate the approach's efficacy.
  • Performance was validated across various scenarios with differing amounts of labeled data.

Conclusions:

  • The anomaly detection framework significantly aids in discovering and diagnosing software performance problems.
  • Highlighting anomalous system call subsequences streamlines the troubleshooting process for developers.
  • The approach offers a scalable solution for performance anomaly detection in complex software.