Compositional Reduction of Performability Models based on Stochastic Process Algebras
نویسندگان
چکیده
Stochastic Process Algebras (SPA) have been proposed as compositional specification formalisms for quantitative models. Here we apply these compositional features to SPAs extended by rewards. State space reduction of performability models can be achieved based on the behaviour-preserving notion of Markov Reward Bisimulation. For a framework extended by immediate actions we develop a new equivalence relation which allows further model reduction. We show that both bisimulations are congruences concerning the composition operators of the SPA, which enables a compositional reduction technique.
منابع مشابه
Advances in Model Representations
We review high-level speciication formalisms for Markovian performability models, thereby emphasising the role of structuring concepts as realised par excellence by stochastic process algebras. Symbolic representations based on decision diagrams are presented, and it is shown that they quite ideally support compositional model construction and analysis.
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