An environment for importance sampling based on stochastic activity networks

Model-based evaluation of reliable distributed and parallel systems is difficult due to the complexity of these systems and the nature of the dependability measures of interest. The complexity creates problems for analytical model solution techniques, and the fact that reliability and availability measures are based on rare events makes traditional simulation methods inefficient. Importance sampling is a well-known technique for improving the efficiency of rare event simulations. However, finding an importance sampling strategy that works well in general is a difficult problem. The best strategy for importance sampling depends on the characteristics of the system and the dependability measure of interest. This fact motivated the development of an environment for importance sampling that would support the wide variety of model characteristics and interesting measures. The environment is based on stochastic activity networks, and importance sampling strategies are specified using the new concept of the importance sampling governor. The governor supports dynamic importance sampling strategies by allowing the stochastic elements of the model to be redefined based on the evolution of the simulation. The utility of the new environment is demonstrated by evaluating the unreliability of a highly dependable fault-tolerant unit used in the well-known MARS architecture. The model is non-Markovian, with Weibull distributed failure times and uniformly distributed repair times