Peter G. Harrison

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Product-form solutions in Markovian process algebra (MPA) are constructed using properties of reversed processes. The compositionality of MPAs is directly exploited, allowing a large class of hierarchically constructed systems to be solved for their state probabilities at equilibrium. The paper contains new results on both reversed stationary Markov(More)
Prograxnming parallel machines is notoriously difficult. Factors contributing to this difficulty include the complexity of concurrency, the effect of resource allocation on performance and the current diversity of parallel machine models. The net result is that effective portability, which depends crucially on the predictability of performance, has been(More)
Stochastic networks defined by a collection of cooperating agents are solved for their equilibrium state probability distribution by a new compositional method. The agents are processes formalised in a Markovian Process Algebra, which enables the reversed stationary Markov process of a cooperation to be determined symbolically under appropriate conditions.(More)
EEcient product form solution is one of the major attractions of queueing networks for performance modelling purposes. These models rely on a form of interaction between nodes in a network which allows them to be solved in isolation, since they behave as if independent up to normalisation. Markovian process algebras (MPA) extend classical process algebras(More)
Semi-Markov processes (SMPs) are expressive tools for modelling concurrent systems; they are a generalisation of Markov processes that allow for arbitrarily distributed sojourn times. This paper presents an iterative technique for passage time and transient analysis of large structurally unrestricted semi-Markov processes. Our method is based on the(More)