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Analysis of Non-Markovian Systems in GreatSPN

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New Frontiers in Quantitative Methods in Informatics (InfQ 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 825))

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Abstract

Markov Regenerative Processes (MRgP) with enabling restriction allow to model stochastic processes where the firing distribution of some events may be specified by a non-Markovian Probability Distribution Function, provided that at most one of these events is enabled in any state of the process. The GreatSPN framework is a collection of tools for the modeling and analysis of systems specified as Stochastic Petri Nets. The paper describes the new features of the MRgP solver of GreatSPN to deal with MRgP processes. The solver supports a rich language for the specification of non-Markovian events, and different solution techniques (explicit, matrix-free, component-based) for the MRgP analysis. The potentiality of the tools are shown on a few examples.

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Authors and Affiliations

  1. Dipartimento di Informatica, Università di Torino, Turin, Italy

    Elvio Gilberto Amparore & Susanna Donatelli

Authors
  1. Elvio Gilberto Amparore
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  2. Susanna Donatelli
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Correspondence to Elvio Gilberto Amparore .

Editor information

Editors and Affiliations

  1. Ca’ Foscari University of Venice, Venice, Italy

    Simonetta Balsamo

  2. Ca’ Foscari University of Venice, Venice, Italy

    Andrea Marin

  3. University of Florence, Florence, Italy

    Enrico Vicario

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Amparore, E.G., Donatelli, S. (2018). Analysis of Non-Markovian Systems in GreatSPN. In: Balsamo, S., Marin, A., Vicario, E. (eds) New Frontiers in Quantitative Methods in Informatics. InfQ 2017. Communications in Computer and Information Science, vol 825. Springer, Cham. https://doi.org/10.1007/978-3-319-91632-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-91632-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91631-6

  • Online ISBN: 978-3-319-91632-3

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