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<p>The authors present a unified framework for simulating Markovian models of highly dependable systems. It is shown that a variance reduction technique called importance sampling can be used to speed up the simulation by many orders of magnitude over standard simulation. This technique can be combined very effectively with regenerative simulation to estimate measures such as steady-state availability and mean time to failure. Moveover, it can be combined with conditional Monte Carlo methods to quickly estimate transient measures such as reliability, expected interval availability, and the distribution of interval availability. The authors show the effectiveness of these methods by using them to simulate large dependability models. They discuss how these methods can be implemented in a software package to compute both transient and steady-state measures simultaneously from the same sample run.</p>
unified framework; simulating Markovian models; highly dependable systems; variance reduction technique; importance sampling; regenerative simulation; steady-state availability; mean time to failure; Monte Carlo methods; transient measures; reliability; software package; steady-state measures; digital simulation; Markov processes; Monte Carlo methods.

A. Goyal, V. Nicola, P. Shahabuddin, P. Heidelberger and P. Glynn, "A Unified Framework for Simulating Markovian Models of Highly Dependable Systems," in IEEE Transactions on Computers, vol. 41, no. , pp. 36-51, 1992.
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