Discrete-Time Leap Method for Stochastic Simulation

In this paper, we present an approach to improve the efficiency of stochastic simulation for large and dense networks. We are using stochastic Petri nets as modelling framework. The underlying continuoustime Markov chain (CTMC) is converted to an equivalent discrete-time Markov chain (DTMC), this itself gains no efficiency. We improve the efficiency via discrete-time leaps, even though this results in an approximate method. The discrete-time leaps are done by applying the maximum firing rule, this reduces drastically the number of steps. The presented algorithm is implemented in our modelling and simulation tool Snoopy, as well as in our advanced analysis and model checking tool MARCIE. We demonstrate the approach on models of different sizes and complexities.