Passage time densities are useful performance measurements in stochastic systems. With them the modeller can extract probabilistic quality-of-service guarantees such as: the probability that the time taken for a network header packet to travel across a heterogeneous network is less than 10ms must be at least 0.95. In this paper, we show how new tools can extract passage time densities and distributions from stochastic models defined in PEPA, a stochastic process algebra. In stochastic process algebras, the synchronisation policy is important for defining how different system components interact. We also show how these passage time results can vary according to which synchronisation strategy is used. We compare results from two popular strategies.
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