Abstract

Stochastic process algebras have become an accepted part of performance modelling over recent years. Because of the advantages of compositionality and flexibility they are increasingly being used to model larger and more complex systems. Therefore tools which support the evaluation of models expressed using stochastic process algebra must be able to utilise the full range of decomposition and solution techniques available. In this paper we study a class of models which do not give rise to a product form solution but can nevertheless be decomposed into their components without loss of generality. We also exemplify the use of the Markovian process algebra PEPA with the spectral expansion technique which enables a class of PEPA models with infinite state space to be solved numerically. 1 Introduction The advantages of using a stochastic process algebra to specify performance models are well documented (see Hillston [9] for example). In brief, a process algebra allows models to be compare...

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