A Matrix-based Method for Analysing Stochastic Process Algebras

This paper demonstrates how three stochastic process algebras can be mapped on to a generally-distributed stochastic transition system. We demonstrate an aggregation technique on these stochastic transition systems and show how this can be implemented as a matrix-analysis method for finding steady-state distributions. We verify that the time complexity of the algorithm is a considerable improvement upon a previous method and discuss how the technique can be used to generate partial steady-state distributions for SPA systems. Keywords Stochastic process algebras, semi-Markov processes, Markov renewal processes, partial evaluation of steady-state distributions, algorithm complexity, matrix-based analysis.

• Type: Conference
• Year: 2000
• Where: ICALP
• Authors: Jeremy T. Bradley, N. J. Davies
• Keywords: ICALP 2000, Programming Languages, Steady-state Distributions, Stochastic Process Algebras, Stochastic Transition Systems