A UNIFIED FRAMEWORK FOR SIMULATING MARKOVIAN MODELS OF HIGHLY DEPENDABLE SYSTEMS

In this paper we present a unified framework for simulating Markovian models of highly dependable systems. Since the failure event is a rare event, the estimation of system dependability measures using standard simulation requires very long simulation runs. We show 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. Moreover, 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. We show the effectiveness of these methods by using them to simulate large dependability models. We also 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.