Speed-up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction

Cheng Feng; Jane Hillston

Speed-up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction

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

Abstract

We present a novel statistical model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. This is achieved by identifying and removing agent types and transitions from the simulation which have only minor impact on the evolution of population dynamics of target agent types specified by the modeller. The error induced on the target agent types can be measured by a normalized coupling coefficient, which is calculated by an error propagation method over a directed relation graph for the PCTMC, using a limited number of simulation runs of the full model. Those agent types and transitions with minor impact are safely removed without incurring a significant error on the simulation result. To demonstrate the approach, we show the usefulness of our statistical reduction method by applying it to 50 randomly generated PCTMC models corresponding to different city bike-sharing scenarios.

Original language	English
Title of host publication	Computer Performance Engineering
Subtitle of host publication	12th European Workshop, EPEW 2015, Madrid, Spain, August 31 - September 1, 2015, Proceedings
Publisher	Springer
Pages	291-305
Number of pages	15
ISBN (Electronic)	978-3-319-23267-6
ISBN (Print)	978-3-319-23266-9
Publication status	Published - 22 Aug 2015
Event	European Performance Engineering Workshop - Madrid, Spain Duration: 31 Aug 2015 → 1 Sept 2015

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin Heidelberg
Volume	9272
ISSN (Print)	0302-9743

Conference

Conference	European Performance Engineering Workshop
Country/Territory	Spain
City	Madrid
Period	31/08/15 → 1/09/15

Keywords / Materials (for Non-textual outputs)

stochastic simulation
model reduction
PCTMC
stochastic process algebra

Access to Document

epew2015_pctmcAccepted author manuscript, 364 KB

QUANTICOL - A Quantitative Approach to Management and Design of Collective and Adaptive Behaviours (RTD)
Hillston, J. (Principal Investigator) & Gilmore, S. (Co-investigator)
EU government bodies
1/04/13 → 31/03/17
Project: Research

Cite this

@inproceedings{ecb240f925ef4a1ead8e82f97a5b1443,

title = "Speed-up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction",

abstract = "We present a novel statistical model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. This is achieved by identifying and removing agent types and transitions from the simulation which have only minor impact on the evolution of population dynamics of target agent types specified by the modeller. The error induced on the target agent types can be measured by a normalized coupling coefficient, which is calculated by an error propagation method over a directed relation graph for the PCTMC, using a limited number of simulation runs of the full model. Those agent types and transitions with minor impact are safely removed without incurring a significant error on the simulation result. To demonstrate the approach, we show the usefulness of our statistical reduction method by applying it to 50 randomly generated PCTMC models corresponding to different city bike-sharing scenarios. ",

keywords = "stochastic simulation, model reduction, PCTMC, stochastic process algebra",

author = "Cheng Feng and Jane Hillston",

year = "2015",

month = aug,

day = "22",

language = "English",

isbn = "978-3-319-23266-9",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "291--305",

booktitle = "Computer Performance Engineering",

address = "United Kingdom",

note = "European Performance Engineering Workshop ; Conference date: 31-08-2015 Through 01-09-2015",

}

Feng, C & Hillston, J 2015, Speed-up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction. in Computer Performance Engineering: 12th European Workshop, EPEW 2015, Madrid, Spain, August 31 - September 1, 2015, Proceedings. Lecture Notes in Computer Science, vol. 9272, Springer, pp. 291-305, European Performance Engineering Workshop, Madrid, Spain, 31/08/15.

Speed-up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction. / Feng, Cheng; Hillston, Jane.
Computer Performance Engineering: 12th European Workshop, EPEW 2015, Madrid, Spain, August 31 - September 1, 2015, Proceedings. Springer, 2015. p. 291-305 (Lecture Notes in Computer Science; Vol. 9272).

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

TY - GEN

T1 - Speed-up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction

AU - Feng, Cheng

AU - Hillston, Jane

PY - 2015/8/22

Y1 - 2015/8/22

N2 - We present a novel statistical model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. This is achieved by identifying and removing agent types and transitions from the simulation which have only minor impact on the evolution of population dynamics of target agent types specified by the modeller. The error induced on the target agent types can be measured by a normalized coupling coefficient, which is calculated by an error propagation method over a directed relation graph for the PCTMC, using a limited number of simulation runs of the full model. Those agent types and transitions with minor impact are safely removed without incurring a significant error on the simulation result. To demonstrate the approach, we show the usefulness of our statistical reduction method by applying it to 50 randomly generated PCTMC models corresponding to different city bike-sharing scenarios.

AB - We present a novel statistical model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. This is achieved by identifying and removing agent types and transitions from the simulation which have only minor impact on the evolution of population dynamics of target agent types specified by the modeller. The error induced on the target agent types can be measured by a normalized coupling coefficient, which is calculated by an error propagation method over a directed relation graph for the PCTMC, using a limited number of simulation runs of the full model. Those agent types and transitions with minor impact are safely removed without incurring a significant error on the simulation result. To demonstrate the approach, we show the usefulness of our statistical reduction method by applying it to 50 randomly generated PCTMC models corresponding to different city bike-sharing scenarios.

KW - stochastic simulation

KW - model reduction

KW - PCTMC

KW - stochastic process algebra

M3 - Conference contribution

SN - 978-3-319-23266-9

T3 - Lecture Notes in Computer Science

SP - 291

EP - 305

BT - Computer Performance Engineering

PB - Springer

T2 - European Performance Engineering Workshop

Y2 - 31 August 2015 through 1 September 2015

ER -

Speed-up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction

Abstract

Publication series

Conference

Keywords / Materials (for Non-textual outputs)

Access to Document

Fingerprint

Projects

QUANTICOL - A Quantitative Approach to Management and Design of Collective and Adaptive Behaviours (RTD)

Cite this