Compositional Approximate Markov Chain Aggregation for PEPA Models

Dimitrios Milios; Stephen Gilmore

doi:10.1007/978-3-642-36781-6_7

Compositional Approximate Markov Chain Aggregation for PEPA Models

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	Computer Performance Engineering
Subtitle of host publication	9th European Workshop, EPEW 2012, Munich, Germany, July 30, 2012, and 28th UK Workshop, UKPEW 2012, Edinburgh, UK, July 2, 2012, Revised Selected Papers
Publisher	Springer
Number of pages	15
ISBN (Electronic)	978-3-642-36781-6
ISBN (Print)	978-3-642-36780-9
DOIs	https://doi.org/10.1007/978-3-642-36781-6_7
Publication status	Published - 30 Jul 2012
Event	European Performance Engineering Workshop - Munich, Germany Duration: 30 Jul 2012 → 30 Jul 2012

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin / Heidelberg
Volume	7587
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Performance Engineering Workshop
Country/Territory	Germany
City	Munich
Period	30/07/12 → 30/07/12

Access to Document

10.1007/978-3-642-36781-6_7

GilmoreAccepted author manuscript, 181 KB

http://epew2012.pst.ifi.lmu.de/pub/Gilmore.pdf

Systems Training in Maths, Informatics and Computational Biology (SySMIC)
Gilmore, S. (Principal Investigator)
BBSRC
1/01/12 → 31/12/16
Project: Research

Cite this

Milios, D., & Gilmore, S. (2012). Compositional Approximate Markov Chain Aggregation for PEPA Models. In Computer Performance Engineering: 9th European Workshop, EPEW 2012, Munich, Germany, July 30, 2012, and 28th UK Workshop, UKPEW 2012, Edinburgh, UK, July 2, 2012, Revised Selected Papers (Lecture Notes in Computer Science; Vol. 7587). Springer. https://doi.org/10.1007/978-3-642-36781-6_7

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title = "Compositional Approximate Markov Chain Aggregation for PEPA Models",

abstract = "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.",

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doi = "10.1007/978-3-642-36781-6_7",

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Milios, D & Gilmore, S 2012, Compositional Approximate Markov Chain Aggregation for PEPA Models. in Computer Performance Engineering: 9th European Workshop, EPEW 2012, Munich, Germany, July 30, 2012, and 28th UK Workshop, UKPEW 2012, Edinburgh, UK, July 2, 2012, Revised Selected Papers. Lecture Notes in Computer Science, vol. 7587, Springer, European Performance Engineering Workshop, Munich, Germany, 30/07/12. https://doi.org/10.1007/978-3-642-36781-6_7

Compositional Approximate Markov Chain Aggregation for PEPA Models. / Milios, Dimitrios; Gilmore, Stephen.
Computer Performance Engineering: 9th European Workshop, EPEW 2012, Munich, Germany, July 30, 2012, and 28th UK Workshop, UKPEW 2012, Edinburgh, UK, July 2, 2012, Revised Selected Papers. Springer, 2012. (Lecture Notes in Computer Science; Vol. 7587).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

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Compositional Approximate Markov Chain Aggregation for PEPA Models

Abstract

Publication series

Conference

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Projects

Systems Training in Maths, Informatics and Computational Biology (SySMIC)

Cite this