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Pizza, M.; Strigini, L.; Bondavalli, A.; Di Giandomenico, F. (1998)
Languages: English
Types: Unknown
Subjects: QA75

Classified by OpenAIRE into

An important practical problem in fault diagnosis is discriminating between permanent faults and transient faults. In many computer systems, the majority of errors are due to transient faults. Many heuristic methods have been used for discriminating between transient and permanent faults; however, we have found no previous work stating this decision problem in clear probabilistic terms. We present an optimal procedure for discriminating between transient and permanent faults, based on applying Bayesian inference to the observed events (correct and erroneous results). We describe how the assessed probability that a module is permanently faulty must vary with observed symptoms. We describe and demonstrate our proposed method on a simple application problem, building the appropriate equations and showing numerical examples. The method can be implemented as a run-time diagnosis algorithm at little computational cost; it can also be used to evaluate any heuristic diagnostic procedure by comparison
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] A. Bondavalli, S. Chiaradonna, F. Di Giandomenico and F. Grandoni, "Discriminating Fault Rate and Persistency to Improve Fault Treatment", in Proc. FTCS-27, Seattle, USA, 1997, pp. 354-362.
    • [2] M. H. DeGroot, "Probability and Statistics", Reading, Mass, Addison-Wesley, 1986.
    • [3] F. Di Giandomenico, L. Strigini, "Adjudicators for Diverse-Redundant Components", Proc. 9th Symp. on Reliable Distributed Systems (SRDS-9), Huntsville, Ala., 1990, pp. 114-123.
    • [4] R. K. Iyer, L. T. Young and P. V. K. Iyer, "Automatic Recognition of Intermittent Failures: An Experimental Study of Field Data", IEEE TC, C-39, pp. 525-537, 1990.
    • [5] J. H. Lala and L. S. Alger, "Hardware and Software FaultTolerance: a Unified Architectural Approach", in Proc. FTCS-18, Tokyo, 1988, pp. 240-245.
    • [6] T.-T. Y. Lin and D. P. Siewiorek, "Error Log Analysis: Statistical Modeling and Heuristic Trend Analysis", IEEE Transactions on Reliability, 39, pp. 419-432, 1990.
    • [7] H. F. Martz and R. A. Waller, "Bayesian Reliability Analysis", New York, John Wiley & Sons, 1982.
    • [8] G. Mongardi, "Dependable Computing for Railway Control Systems", in Proc. DCCA-3, Mondello, Italy, 1993, pp. 255-277.
    • [9] M. Pizza, "Un approccio bayesiano alla diagnosi di guasti hardware transitori e permanenti in sistemi di elaborazione", Thesis, University of Pisa, 1996.
    • [10] M. Pizza, L. Strigini, A. Bondavalli and F. Di. Giandomenico, "Bayesian Diagnosis of Transient vs Permanent Faults", Centre for Software Reliability Technical Report, 1998. Available from the authors or at: http://www.csr.city.ac.uk/papers/1998.html#pizza_1.
    • [11] D. P. Siewiorek and R. S. Schwartz, "Reliable Computer Systems Design and Evaluation", Bedford, MA, Digital Press, 1992.
    • [12] L. Spainhower, J. Isenberg, R. Chillarege and J. Berding, "Design for Fault-Tolerance in System ES/9000 Model 900", in Proc. FTCS-22, Boston, Massachusetts, 1992, pp. 38-47.
    • [13] N. N. Tendolkar and R. L. Swann, "Automated Diagnostic Methodology for the IBM 3081 Processor Complex", IBM J. Res. Develop., 26, pp. 78-88, 1982.
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