Time Scale Decomposition of Stochastic Process Algebra Models

Realistic models of computer and communication systems result in large, complex performance models. Compositionality, offered by stochastic process algebra constructs a model from submodels which are smaller and more tractable. We present a technique to exploit this structure in order to enhance the solution of the model by decomposition of the underlying Markov process. The decomposition under consideration is time scale decomposition, based on Courtois's near complete decomposability. This work has been influenced by related work on stochastic Petri nets and presents a major advancement related to a preliminary version of this technique already presented at a workshop of this series. In particular, a transformation technique is developed which is based on a delayed choice operator and preserves equivalence. 1 Introduction Stochastic process algebras (SPA) are formal description techniques by which systems are modelled as an interaction of autonomous agents or components who engage i..