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Many modern computer and communication systems result in large, complex performance models. The compositional approach o ered by stochastic process algebra constructs a model from submodels which are smaller and more easily understood. This gives the model a clear component-based structure. In this paper we present cases when this structure may be used to inform the solution of the model, leading to an e cient solution based on a decomposition of the underlying Markov process. The decompositiondoi:10.1093/comjnl/38.7.566 fatcat:s4kltebflvcz3hz447g6azidti