Compositional Markovian Modelling Using a Process Algebra

Jane Hillston
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Jane Hillston: University of Edinburgh

Chapter 12 in Computations with Markov Chains, 1995, pp 177-196 from Springer

Abstract: Abstract We introduce a stochastic process algebra, PEPA, as a high-level modelling paradigm for continuous time Markov chains (CTMC). Process algebras are mathematical theories which model concurrent systems by their algebra and provide apparatus for reasoning about the structure and behaviour of the model. Recent extensions of these algebras, associating random variables with actions, make the models also amenable to Markovian analysis. A compositional structure is inherent in the PEPA language. As well as the clear advantages that this offers for model construction, we demonstrate how this compositionality may be exploited to reduce the state space of the CTMC. This leads to an exact aggregation based on lumpability.

Keywords: Action Type; Shared Activity; Process Algebra; Continuous Time Markov Chain; Reward Structure (search for similar items in EconPapers)
Date: 1995
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4615-2241-6_12

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DOI: 10.1007/978-1-4615-2241-6_12

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