Performance analysis of stochastic process algebra models using stochastic simulation
Jeremy T. Bradley, Stephen T. Gilmore, Nigel V. Thomas · 2006
We present a translation of a generic stochastic process algebra model into a form suitable for stochastic simulation. By systematically generating rate equations from a process description, we can use tools developed for chemical and biochemical reaction analysis to provide timeseries output for models with state spaces of O(10 10000) and beyond. We apply these techniques to a significant case study: that of a secure electronic voting protocol. particular state at a given time, t. Here, we use a stochastic simulator to provide trace executions of the underlying rate equations and use multiple traces to provide confidence intervals for being in a given state. In this paper, we present a formal transformation from the stochastic process algebra, PEPA [16], to an equivalent rate equation description. We use an electronic voting system case study to show that a state-space of many times the size of an explicit state-space technique can be analysed. 1.1 Background: Stochastic simulation 1