eprintid: 2601
rev_number: 5
eprint_status: archive
userid: 6
dir: disk0/00/00/26/01
datestamp: 2015-02-11 14:14:02
lastmod: 2015-02-11 14:14:02
status_changed: 2015-02-11 14:14:02
type: article
metadata_visibility: show
creators_name: Tribastone, Mirco
creators_name: Gilmore, Stephen
creators_name: Hillston, Jane
creators_id: mirco.tribastone@imtlucca.it
creators_id: 
creators_id: 
title: Scalable differential analysis of process algebra models
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
keywords: Markov processes; Modeling and prediction; ordinary differential equations

abstract: 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.
date: 2012-01
publication: IEEE Transactions on Software Engineering
volume: 38
number: 1
publisher: IEEE Computer Society
pagerange: 205-219
id_number: 10.1109/TSE.2010.82
refereed: TRUE
issn: 0098-5589
official_url: ttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5567115&isnumber=6141067
citation:   Tribastone, Mirco and Gilmore, Stephen and Hillston, Jane  Scalable differential analysis of process algebra models.  IEEE Transactions on Software Engineering, 38 (1).  pp. 205-219.  ISSN 0098-5589      (2012)