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Importance sampling is a well known technique that can be used for either variance reduction or obtaining performance estimates at multiple input parameter settings from a single simulation run (“what if” simulations). However, in queueing simulations, there is an essentially unique asymptotically efficient importance sampling distribution for estimating the probability y of certain rare events (e.g., buffer overflows). Furthermore, this unique distribution depends critically on the inputs of the model, thereby making it difficult to obtain good “what if” estimates from a single run. (An example of this is using a single run to estimate the mean time until buffer overflow at multiple arrival rates. ) In this paper, we show that a single importance sampling distribution can effectively be used for both variance reduction and “what if” simulation of certain rare events in models of highly dependable systems.

Simultaneous and efficient simulation of highly dependable systems with different underlying distributions