Techniques for fast simulation of models of highly dependable systems

• Published in: IEEE transactions on reliability Vol. 50; no. 3; pp. 246 - 264
• Main Authors: Nicola, V.F., Shahabuddin, P., Nakayama, M.K.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2001; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Availability; Degradation; Discrete event simulation; Length measurement; Monte Carlo methods; Operating systems; Particle measurements; Sampling techniques; Steady-state; Studies; Time measurement
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/24.974122

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 nonMarkov models of highly dependable systems.