Probabilistic model-driven recovery in distributed systems

Kaustubh R. Joshi, Matti A. Hiltunen, William H. Sanders, Richard D. Schlichting

Research output: Contribution to journalArticlepeer-review


Automatic system monitoring and recovery has the potential to provide effective, low-cost ways to improve dependability in distributed software systems. However, automating recovery is challenging in practice because accurate fault diagnosis is hampered by monitoring tools and techniques that often have low fault coverage, poor fault localization, detection delays, and false positives. In this paper, we present a holistic model-based approach that overcomes these challenges and enables automatic recovery in distributed systems. To do so, it uses theoretically sound techniques including Bayesian estimation and Markov decision theory to provide controllers that choose good, if not optimal, recovery actions according to a user-defined optimization criteria. By combining monitoring and recovery, the approach realizes benefits that could not have been obtained by using them in isolation. We experimentally validate our framework by fault injection on realistic e-commerce systems.

Original languageEnglish (US)
Article number5590252
Pages (from-to)913-928
Number of pages16
JournalIEEE Transactions on Dependable and Secure Computing
Issue number6
StatePublished - 2011


  • Bayesian.
  • Fault tolerance
  • adaptive systems
  • diagnosis
  • distributed systems
  • monitoring
  • recovery

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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