《简明随机过程代数讲义（英文版－爱丁堡大学）》.pdfVIP

Stochastic Process Algebras Allan Clark, Stephen Gilmore, Jane Hillston, and Mirco Tribastone LFCS, School of Informatics, University of Edinburgh Abstract. In this tutorial we give an introduction to stochastic process algebras and their use in performance modelling, with a focus on the PEPA formalism. A brief introduction is given to the motivations for extending classical process algebra with stochastic times and probabilis- tic choice. We then present an introduction to the modelling capabil- ities of the formalism and the tools available to support Markovian based analysis. The chapter is illustrated throughout by small examples, demonstrating the use of the formalism and the tools. 1 Introduction Process algebras emerged as a modelling technique for the functional analysis of concurrent systems approximately twenty years ago. Over the last 17 years there have been several attempts to take advantage of the attractive features of this modelling paradigm within the field of performance evaluation. Stochastic process algebras (SPA) were first proposed as a tool for perfor- mance and dependability modelling in 1990 [1]. At that time there was already a plethora of techniques for constructing performance models so the introduction of another one could have been deemed unnecessary if it were not for the fact that SPA offered something new—formally defined compositionality. Queueing networks, which have been widely used for performance modelling for more than thirty years, have an inherent compositionality but this is implicit and infor- mal. Stochastic extensions of Petri nets have a semantic model but, in general, no clear compositional structure. In the process algebra the compositionality is explicit—provided by the combinator