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A reward-based performability modelling of a fault-tolerant safety–critical system

Author

Listed:
  • Shakeel Ahamad

    (Jawaharlal Nehru University)

  • Ratneshwer Gupta

    (Jawaharlal Nehru University)

Abstract

Nowadays, various computer system carries out critical functions. The failure of these systems leads to unacceptable loss. Such systems are called Safety–Critical Systems (SCS). The Performance and Reliability of SCS should be high. So, the combined study of performance and reliability (called Performability) is an important issue. The testing of the system is also used to improve its performance. However, some issues might not be addressed in the testing procedure. Formal verification is used for developing secure software. In most of the research work, performability is obtained by operational systems or fail repair systems. Some studies have considered the fail-repair, including fault-tolerant systems. Safety–critical systems generally have fault-tolerant mechanisms to minimize the severity of the failure. This paper studies the safety–critical system's performability using the continuous-time Markov chain (CTMC) with a reward called the Markov reward model (MRM), keeping in mind the fail-repair, fault-tolerant characteristics of the systems. The various parameters of the performability have been analyzed. For mathematical calculation, python language is used. The case study illustrates the proposed approach.

Suggested Citation

  • Shakeel Ahamad & Ratneshwer Gupta, 2023. "A reward-based performability modelling of a fault-tolerant safety–critical system," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(6), pages 2218-2234, December.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:6:d:10.1007_s13198-023-02055-3
    DOI: 10.1007/s13198-023-02055-3
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    References listed on IDEAS

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    1. Mo, Yuchang & Liu, Yu & Cui, Lirong, 2018. "Performability analysis of multi-state series-parallel systems with heterogeneous components," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 48-56.
    2. Alexandre Martins & Balduíno Mateus & Inácio Fonseca & José Torres Farinha & João Rodrigues & Mateus Mendes & António Marques Cardoso, 2023. "Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models," Energies, MDPI, vol. 16(6), pages 1-26, March.
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