Avoiding redundancies in the Proxel method

Abstract

The simulation of discrete stochastic systems is used to make predictions on system behaviour. Its most widely used technique, discrete event simulation, computes possible simulation results by using random numbers. Consequently, these results are also only random numbers. Alternative state space–based simulation techniques can directly compute the actual system behaviour, but are computationally infeasible for bigger models. In this work, we improve the state space–based Proxel simulation method by avoiding some of its redundancies through clustering of discrete states. Our experiments demonstrate a speedup by a factor of two to five for realistic models, without any loss in accuracy. If no redundancies in the model can be exploited, the method only incurs a small computational overhead. Our approach thus has the potential of making deterministic state space–based analysis of existing models more efficient, and of enabling the analysis of bigger models that more accurately reflect real systems.