Scalable Differential Analysis of Process Algebra Models

Mirco Tribastone, Stephen Gilmore, Jane Hillston

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The exact performance analysis of large-scale software systems with discrete-state approaches is difficult because of the well-known problem of state-space explosion. This paper considers this problem with regard to the stochastic process algebra PEPA, presenting a deterministic approximation to the underlying Markov chain model based on ordinary differential equations. The accuracy of the approximation is assessed by means of a substantial case study of a distributed multithreaded application.
Original languageEnglish
Pages (from-to)205-219
Number of pages15
JournalIEEE Transactions on Software Engineering
Issue number1
Early online date9 Sept 2010
Publication statusPublished - 2012

Keywords / Materials (for Non-textual outputs)

  • Markov processes
  • Modeling and prediction
  • ordinary differential equations


Dive into the research topics of 'Scalable Differential Analysis of Process Algebra Models'. Together they form a unique fingerprint.

Cite this