Bounding Average Time Separations of Events in Stochastic Timed Petri Nets with Choice

This paper presents a technique to estimate the average time separation of events (TSE) in stochas-tic timed Petri nets that can model time-independent choice and have arbitrary delay distributions associated with places. The approach analyzes nite net unfold-ings to derive closed-form expressions for lower and upper bounds on the average TSE, which can be ef-ciently evaluated using standard statistical methods. The mean of the derived upper and lower bounds thus provides an estimate of the average TSE which has a well-deened error bound. Moreover, we can often make the error arbitrarily small by analyzing larger net un-foldings at the cost of additional run-time. Experiments on several asynchronous systems demonstrate the quality of our estimate and the eeciency of the technique. The experiments include the performance analysis of a full-scale Petri net model of Intel's asynchronous instruction length decoding and steering unit RAPPID containing over 900 transitions and 500 places.