Rare event simulation for highly dependable systems with fast repairs

Research output: Contribution to journalArticle

  • 5 Citations

Abstract

Probabilistic model checking has been used recently to assess, among others, dependability measures for a variety of systems. However, the numerical methods employed, such as those supported by model checking tools such as PRISM and MRMC, suffer from the state-space explosion problem. The main alternative is statistical model checking, which uses standard Monte Carlo simulation, but this performs poorly when small probabilities need to be estimated. Therefore, we propose a method based on importance sampling to speed up the simulation process in cases where the failure probabilities are small due to the high speed of the system’s repair units. This setting arises naturally in Markovian models of highly dependable systems. We show that our method compares favourably to standard simulation, to existing importance sampling techniques, and to the numerical techniques of PRISM.
LanguageUndefined
Pages336-355
Number of pages20
JournalPerformance evaluation
Volume69
Issue number7-8
DOIs
StatePublished - Jul 2012

Keywords

  • EWI-21600
  • Statistical Model Checking
  • Importance sampling
  • IR-80904
  • Rare events
  • METIS-287863
  • Dependable systems

Cite this

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title = "Rare event simulation for highly dependable systems with fast repairs",
abstract = "Probabilistic model checking has been used recently to assess, among others, dependability measures for a variety of systems. However, the numerical methods employed, such as those supported by model checking tools such as PRISM and MRMC, suffer from the state-space explosion problem. The main alternative is statistical model checking, which uses standard Monte Carlo simulation, but this performs poorly when small probabilities need to be estimated. Therefore, we propose a method based on importance sampling to speed up the simulation process in cases where the failure probabilities are small due to the high speed of the system’s repair units. This setting arises naturally in Markovian models of highly dependable systems. We show that our method compares favourably to standard simulation, to existing importance sampling techniques, and to the numerical techniques of PRISM.",
keywords = "EWI-21600, Statistical Model Checking, Importance sampling, IR-80904, Rare events, METIS-287863, Dependable systems",
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journal = "Performance evaluation",
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Rare event simulation for highly dependable systems with fast repairs. / Reijsbergen, D.P.; de Boer, Pieter-Tjerk; Scheinhardt, Willem R.W.; Haverkort, Boudewijn R.H.M.

In: Performance evaluation, Vol. 69, No. 7-8, 07.2012, p. 336-355.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Rare event simulation for highly dependable systems with fast repairs

AU - Reijsbergen,D.P.

AU - de Boer,Pieter-Tjerk

AU - Scheinhardt,Willem R.W.

AU - Haverkort,Boudewijn R.H.M.

N1 - eemcs-eprint-21600

PY - 2012/7

Y1 - 2012/7

N2 - Probabilistic model checking has been used recently to assess, among others, dependability measures for a variety of systems. However, the numerical methods employed, such as those supported by model checking tools such as PRISM and MRMC, suffer from the state-space explosion problem. The main alternative is statistical model checking, which uses standard Monte Carlo simulation, but this performs poorly when small probabilities need to be estimated. Therefore, we propose a method based on importance sampling to speed up the simulation process in cases where the failure probabilities are small due to the high speed of the system’s repair units. This setting arises naturally in Markovian models of highly dependable systems. We show that our method compares favourably to standard simulation, to existing importance sampling techniques, and to the numerical techniques of PRISM.

AB - Probabilistic model checking has been used recently to assess, among others, dependability measures for a variety of systems. However, the numerical methods employed, such as those supported by model checking tools such as PRISM and MRMC, suffer from the state-space explosion problem. The main alternative is statistical model checking, which uses standard Monte Carlo simulation, but this performs poorly when small probabilities need to be estimated. Therefore, we propose a method based on importance sampling to speed up the simulation process in cases where the failure probabilities are small due to the high speed of the system’s repair units. This setting arises naturally in Markovian models of highly dependable systems. We show that our method compares favourably to standard simulation, to existing importance sampling techniques, and to the numerical techniques of PRISM.

KW - EWI-21600

KW - Statistical Model Checking

KW - Importance sampling

KW - IR-80904

KW - Rare events

KW - METIS-287863

KW - Dependable systems

U2 - 10.1016/j.peva.2011.11.004

DO - 10.1016/j.peva.2011.11.004

M3 - Article

VL - 69

SP - 336

EP - 355

JO - Performance evaluation

T2 - Performance evaluation

JF - Performance evaluation

SN - 0166-5316

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ER -