This book presents a stochastic extension of process algebra called PEPA that is suitable for specifying a Markov process, which can then be applied to performance modeling. This method, which the author illustrates with case studies taken from the area of communication systems, can readily be used to construct a variety of models that can be analyzed using standard numerical techniques. One of the major advantages of PEPA over the standard methods for specifying stochastic performance models is the inherent apparatus for reasoning about the structure and behavior of models. In the later chapters this apparatus is exploited to define four equivalence relations over PEPA components. Each of these notions of equivalence has intrinsic interest from a process algebra perspective. However, the author also demonstrates them to be useful in a performance modeling context. To conclude the book, the author surveys recent results in the area and discusses open questions. This book will appeal...

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