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Model-Based Design of Resilient Systems Using Quantitative Risk Assessment

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Verification and Evaluation of Computer and Communication Systems (VECoS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12519))

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Abstract

Fault detection, isolation and recovery (FDIR) subsystems are an accepted technique to make safety-critical systems resilient against faults and failures. Yet, these subsystems should be devised only for those faults that violate the system’s requirements, while providing a correct approach such that requirements are met again. As a consequence, the obtained system is minimal, although complete, and robust both with respect to safety and performance requirements. In this paper, we propose a systematic and automated approach based on formal methods that includes (1) the evaluation of the relevance of faults based on quantitative risk assessment, and (2) the validation of system robustness by statistical model checking. We apply this approach on an excerpt of a real-life autonomous robotics case study, and we report on the implementation and results obtained with the \(\mathcal {S}\text {BIP}\) framework.

This work has been supported by the EU’s H2020 research and innovation programme under grant agreement #730080 (ESROCOS) and #700665 (CITADEL).

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Notes

  1. 1.

    BIP stands for Behavior - Interaction - Priority.

  2. 2.

    We refer the readers to [24] for the formal definition of the stochastic real-time BIP.

  3. 3.

    The contribution presented in this paper has been used for the development of the FDIR components in the robotics systems scenarios presented in [23, 25].

  4. 4.

    The suffixes out, in and return used in Fig. 3 are modeling the directionality of the requests. Out models that the component sends the request. In models that the component receives the request. Return models that the action associated with the request has finished executing.

  5. 5.

    Notice that the values for P, D, \(\textit{MIAT}\), and size are part of the system specification.

  6. 6.

    Model sources are available at https://drive.google.com/file/d/1oN90ZraClQxAH5hHE2tl7t2IMsZVzo7L/view?usp=drivesdk.

  7. 7.

    The system architecture and specification, \(\mathsf {Watchdog}\) included, have been provided in the frame of this case study such that the used resources (e.g., number of components and threads) are minimal.

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Authors and Affiliations

  1. Huawei Technologies France, Grenoble, France

    Braham Lotfi Mediouni, Ayoub Nouri & Saddek Bensalem

  2. GMV Aerospace and Defence, Madrid, Spain

    Iulia Dragomir

  3. Univ. Grenoble Alpes, CNRS, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), VERIMAG, 38000, Grenoble, France

    Saddek Bensalem

Authors
  1. Braham Lotfi Mediouni
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  2. Iulia Dragomir
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Correspondence to Braham Lotfi Mediouni .

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Editors and Affiliations

  1. CEA List, DSCIN, LECA, Gif-sur-Yvette, France

    Belgacem Ben Hedia

  2. Academia Sinica, Taipei, Taiwan

    Yu-Fang Chen

  3. Xidian University, Xi'an, China

    Gaiyun Liu

  4. Xi'an University of Science and Technology, Xi’an, China

    Zhenhua Yu

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Mediouni, B.L., Dragomir, I., Nouri, A., Bensalem, S. (2020). Model-Based Design of Resilient Systems Using Quantitative Risk Assessment. In: Ben Hedia, B., Chen, YF., Liu, G., Yu, Z. (eds) Verification and Evaluation of Computer and Communication Systems. VECoS 2020. Lecture Notes in Computer Science(), vol 12519. Springer, Cham. https://doi.org/10.1007/978-3-030-65955-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-65955-4_11

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