Automated known problem diagnosis with event traces

  • Authors:

  • Chun Yuan;Ni Lao;Ji-Rong Wen;Jiwei Li;Zheng Zhang;Yi-Min Wang;Wei-Ying Ma

  • Affiliations:

  • Microsoft Research Asia, Sigma Center, Beijing, China;Tsinghua University, Beijing, China;Microsoft Research Asia, Sigma Center, Beijing, China;University of Science and Technology of China, Anhui, China;Microsoft Research Asia, Sigma Center, Beijing, China;Microsoft Research, Redmond, WA;Microsoft Research Asia, Sigma Center, Beijing, China

  • Venue:
  • Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
  • Year:
  • 2006

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Abstract

Computer problem diagnosis remains a serious challenge to users and support professionals. Traditional troubleshooting methods relying heavily on human intervention make the process inefficient and the results inaccurate even for solved problems, which contribute significantly to user's dissatisfaction. We propose to use system behavior information such as system event traces to build correlations with solved problems, instead of using only vague text descriptions as in existing practices. The goal is to enable automatic identification of the root cause of a problem if it is a known one, which would further lead to its resolution. By applying statistical learning techniques to classifying system call sequences, we show our approach can achieve considerable accuracy of root cause recognition by studying four case examples.