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On Model-Based Performance Analysis of Collective Adaptive Systems

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Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning (ISoLA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13703))

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Abstract

This paper fosters the analysis of performance properties of collective adaptive systems (CAS) since such properties are of paramount relevance practically in any application. We compare two recently proposed approaches: the first is based on generalised stochastic petri nets derived from the system specification; the second is based on queueing networks derived from suitable behavioural abstractions. We use a case study based on a scenario involving autonomous robots to discuss the relative merit of the approaches. Our experimental results assess a mean absolute percentage error lower than 4% when comparing model-based performance analysis results derived from two different quantitative abstractions for CAS.

Work partly funded by MIUR PRIN projects 2017FTXR7S IT MATTERS (Methods and Tools for Trustworthy Smart Systems) and 2017TWRCNB SEDUCE (Designing Spatially Distributed Cyber-Physical Systems under Uncertainty).

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Notes

  1. 1.

    We do not consider forking and joining points of parallel composition (represented by -gates) since this feature is not used in our case study.

  2. 2.

    The fact that identifiers are unique is not built-in in our model; in principle there could be more doors with the same identifier.

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Authors and Affiliations

  1. Gran Sasso Science Institute, L’Aquila, Italy

    Maurizio Murgia, Riccardo Pinciroli, Catia Trubiani & Emilio Tuosto

Authors
  1. Maurizio Murgia
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  2. Riccardo Pinciroli
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  3. Catia Trubiani
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  4. Emilio Tuosto
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Correspondence to Emilio Tuosto .

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Editors and Affiliations

  1. University of Limerick, CSIS and Lero, Limerick, Ireland

    Tiziana Margaria

  2. TU Dortmund, Dortmund, Germany

    Bernhard Steffen

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Murgia, M., Pinciroli, R., Trubiani, C., Tuosto, E. (2022). On Model-Based Performance Analysis of Collective Adaptive Systems. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. ISoLA 2022. Lecture Notes in Computer Science, vol 13703. Springer, Cham. https://doi.org/10.1007/978-3-031-19759-8_17

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  • DOI: https://doi.org/10.1007/978-3-031-19759-8_17

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