Exploiting Structure in Solution: Decomposing Compositional Models

Since their introduction in the early 1990s, compositionality has been reported as one of the major attractions of stochastic process algebras. The benefits that compositionality provides for model construction are readily apparent and have been demonstrated in numerous case studies. Early research on the compositionality of the languages focused on how the inherent structure could be used, in conjunction with equivalence relations, for model simplification and aggregation. In this chapter we consider how far we have been able to take advantage of compositionality when it comes to solving the Markov process underlying a Markovian process algebra model.