Techniques for fast simulation of models of highly dependable systems

Victor F. Nicola, Perwez Shahabuddin, Marvin K. Nakayama

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

With the ever-increasing complexity and requirements of highly dependable systems, their evaluation during design and operation is becoming more crucial. Realistic models of such systems are often not amenable to analysis using conventional analytic or numerical methods. Therefore, analysts and designers turn to simulation to evaluate these models. However, accurate estimation of dependability measures of these models requires that the simulation frequently observes system failures, which are rare events in highly dependable systems. This renders ordinary simulation impractical for evaluating such systems. To overcome this problem, simulation techniques based on importance sampling have been developed, and are very effective in certain settings. When importance sampling works well, simulation run lengths can be reduced by several orders of magnitude when estimating transient as well as steady-state dependability measures. This paper reviews some of the importance-sampling techniques that have been developed in recent years to estimate dependability measures efficiently in Markov and non-Markov models of highly dependable systems.

Original languageEnglish (US)
Pages (from-to)246-264
Number of pages19
JournalIEEE Transactions on Reliability
Volume50
Issue number3
DOIs
StatePublished - Sep 2001

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Electrical and Electronic Engineering

Keywords

  • Highly dependable system
  • Importance sampling
  • Markov chain
  • Simulation
  • Steady-state dependability measure
  • Transient dependability measure

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