Snitch: interactive decision trees for troubleshooting misconfigurations

  • Authors:
  • James Mickens;Martin Szummer;Dushyanth Narayanan

  • Affiliations:
  • University of Michigan, Ann Arbor, MI;Microsoft Research, Cambridge, UK;Microsoft Research, Cambridge, UK

  • Venue:
  • SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
  • Year:
  • 2007

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Abstract

Troubleshooting misconfigurations of modern applications is difficult due to their large and complex state. Snitch is a prototype tool that assists human troubleshooters by finding relationships between application state and subsequent faults. It correlates configuration state and application errors across many machines and users, and across long periods of time. Snitch aids the human expert in extracting patterns from this rich but enormous data set by building decision trees pinpointing potential configuration problems. We applied Snitch to 114 GB of configuration traces from 151 machines over 567 days. We illustrate how Snitch can suggest misconfigurations in case studies of two Windows applications: Messenger and Outlook.