Compositional Approximate Markov Chain Aggregation for PEPA Models

Dimitrios Milios, Stephen Gilmore

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Approximate Markov chain aggregation involves the construction of a smaller Markov chain that approximates the behaviour of a given chain. We discuss two different approaches to obtain a nearly optimal partition of the state-space, based on different notions of approximate state equivalence. Both approximate aggregation methods require an explicit representation of the transition matrix, a fact that renders them inefficient for large models. The main objective of this work is to investigate the possibility of compositionally applying such an approximate aggregation technique. We make use of the Kronecker representation of PEPA models, in order to aggregate the state-space of components rather than of the entire model.
Original languageEnglish
Title of host publicationComputer Performance Engineering
Subtitle of host publication9th European Workshop, EPEW 2012, Munich, Germany, July 30, 2012, and 28th UK Workshop, UKPEW 2012, Edinburgh, UK, July 2, 2012, Revised Selected Papers
PublisherSpringer-Verlag GmbH
Number of pages15
ISBN (Electronic)978-3-642-36781-6
ISBN (Print)978-3-642-36780-9
Publication statusPublished - 30 Jul 2012
EventEuropean Performance Engineering Workshop - Munich, Germany
Duration: 30 Jul 201230 Jul 2012

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Performance Engineering Workshop


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