Performability assessment by model checking of Markov reward models

Christel Baier, L. Cloth, Boudewijn R.H.M. Haverkort, H. Hermanns, Joost P. Katoen

Research output: Contribution to journalArticleAcademicpeer-review

21 Citations (Scopus)

Abstract

This paper describes efficient procedures for model checking Markov reward models, that allow us to evaluate, among others, the performability of computer-communication systems. We present the logic CSRL (Continuous Stochastic Reward Logic) to specify performability measures. It provides flexibility in measure specification and paves the way for the numerical evaluation of a wide variety of performability measures. The formal measure specification in CSRL also often helps in reducing the size of the Markov reward models that need to be numerically analysed. The paper presents background on Markov-reward models, as well as on the logic CSRL (syntax and semantics), before presenting an important duality result between reward and time. We discuss CSRL model-checking algorithms, and present five numerical algorithms and their computational complexity for verifying time- and reward-bounded until-properties, one of the key operators in CSRL. The versatility of our approach is illustrated through a performability case study.
Original languageUndefined
Pages (from-to)1-36
Number of pages36
JournalFormal methods in system design
Volume36
Issue number1
DOIs
Publication statusPublished - 2010

Keywords

  • EWI-18214
  • IR-72601
  • METIS-270941
  • Model checking · Performability · Markov reward models

Cite this

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title = "Performability assessment by model checking of Markov reward models",
abstract = "This paper describes efficient procedures for model checking Markov reward models, that allow us to evaluate, among others, the performability of computer-communication systems. We present the logic CSRL (Continuous Stochastic Reward Logic) to specify performability measures. It provides flexibility in measure specification and paves the way for the numerical evaluation of a wide variety of performability measures. The formal measure specification in CSRL also often helps in reducing the size of the Markov reward models that need to be numerically analysed. The paper presents background on Markov-reward models, as well as on the logic CSRL (syntax and semantics), before presenting an important duality result between reward and time. We discuss CSRL model-checking algorithms, and present five numerical algorithms and their computational complexity for verifying time- and reward-bounded until-properties, one of the key operators in CSRL. The versatility of our approach is illustrated through a performability case study.",
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Performability assessment by model checking of Markov reward models. / Baier, Christel; Cloth, L.; Haverkort, Boudewijn R.H.M.; Hermanns, H.; Katoen, Joost P.

In: Formal methods in system design, Vol. 36, No. 1, 2010, p. 1-36.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Performability assessment by model checking of Markov reward models

AU - Baier, Christel

AU - Cloth, L.

AU - Haverkort, Boudewijn R.H.M.

AU - Hermanns, H.

AU - Katoen, Joost P.

N1 - 10.1007/s10703-009-0088-7

PY - 2010

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AB - This paper describes efficient procedures for model checking Markov reward models, that allow us to evaluate, among others, the performability of computer-communication systems. We present the logic CSRL (Continuous Stochastic Reward Logic) to specify performability measures. It provides flexibility in measure specification and paves the way for the numerical evaluation of a wide variety of performability measures. The formal measure specification in CSRL also often helps in reducing the size of the Markov reward models that need to be numerically analysed. The paper presents background on Markov-reward models, as well as on the logic CSRL (syntax and semantics), before presenting an important duality result between reward and time. We discuss CSRL model-checking algorithms, and present five numerical algorithms and their computational complexity for verifying time- and reward-bounded until-properties, one of the key operators in CSRL. The versatility of our approach is illustrated through a performability case study.

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KW - IR-72601

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