Automatic misconfiguration troubleshooting with peerpressure

  • Authors:
  • Helen J. Wang;John C. Platt;Yu Chen;Ruyun Zhang;Yi-Min Wang

  • Affiliations:
  • Microsoft Research;Microsoft Research;Microsoft Research;Microsoft Research;Microsoft Research

  • Venue:
  • OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
  • Year:
  • 2004

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Abstract

Technical support contributes 17% of the total cost of ownership of today's desktop PCs [25]. An important element of technical support is troubleshooting miscon-figured applications. Misconfiguration troubleshooting is particularly challenging, because configuration information is shared and altered by multiple applications. In this paper, we present a novel troubleshooting system: PeerPressure, which uses statistics from a set of sample machines to diagnose the root-cause misconfigurations on a sick machine. This is in contrast with methods that require manual identification on a healthy machine for diagnosing misconfigurations [30]. The elimination of this manual operation makes a significant step towards automated misconfiguration troubleshooting. In PeerPressure, we introduce a ranking metric for misconfiguration candidates. This metric is based on empirical Bayesian estimation. We have prototyped a PeerPressure troubleshooting system and used a database of 87 machine configuration snapshots to evaluate its performance. With 20 real-world troubleshooting cases, PeerPressure can effectively pinpoint the root-cause misconfigurations for 12 of these cases. For the remaining cases, PeerPressure significantly narrows down the number of root-cause candidates by three orders of magnitude.