Performability assessment by model checking of Markov reward models

Christel Baier, Lucia Cloth, Boudewijn R. Haverkort, Holger 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 languageEnglish
    Pages (from-to)1-36
    Number of pages36
    JournalFormal methods in system design
    Volume36
    Issue number1
    DOIs
    Publication statusPublished - 2010

    Fingerprint

    Performability
    Model checking
    Reward
    Model Checking
    Logic
    Specifications
    Computational complexity
    Communication systems
    Semantics
    Model
    Specification
    Numerical Algorithms
    Communication Systems
    Duality
    Computational Complexity
    Flexibility

    Keywords

    • 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, Lucia; Haverkort, Boudewijn R.; Hermanns, Holger; 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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    AU - Baier, Christel

    AU - Cloth, Lucia

    AU - Haverkort, Boudewijn R.

    AU - Hermanns, Holger

    AU - Katoen, Joost P.

    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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