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Performability analysis of multi-state series-parallel systems with heterogeneous components

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  • Mo, Yuchang
  • Liu, Yu
  • Cui, Lirong

Abstract

Motivated by many networked computing systems used in data centers, Grid or Cloud computing infrastructures, and spatially distributed wireless sensor networks configured in aerospace and military industries, this paper considers a multi-state series-parallel system where n heterogeneous components can possess multiple states with different performance rates. Performability is concerned with the probability of such a system performing at a cumulative system performance W characterized in terms of the minimum of all cumulative subsystem performance Gj (1 ≤ j ≤ m), Gj is the sum of the performance rates of components belonging to the jth subsystem. This paper proposes a multi-valued decision diagram (MDD)-based approach to model and evaluate the concerned performability. A single compact MDD model is constructed by sharing all isomorphic model structures involved in different cumulative system performance. The MDD model can be reused when different versions of component state probability distributions are evaluated, thus it can facilitate solutions to further problems that require executing numerous iterations of performability evaluations, for example, the redundancy and/or reliability optimization problems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:171:y:2018:i:c:p:48-56
    DOI: 10.1016/j.ress.2017.10.023
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    Citations

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    Cited by:

    1. Schäfer, Lukas & García, Sergio & Srithammavanh, Vassili, 2018. "Simplification of inclusion–exclusion on intersections of unions with application to network systems reliability," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 23-33.
    2. Zhang, Xiaoyu & Xu, Maochao & Da, Gaofeng & Zhao, Peng, 2021. "Ensuring confidentiality and availability of sensitive data over a network system under cyber threats," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Dembińska, Anna & Eryilmaz, Serkan, 2021. "Discrete time series–parallel system and its optimal configuration," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Chen, Yiming & Liu, Yu & Jiang, Tao, 2021. "Optimal maintenance strategy for multi-state systems with single maintenance capacity and arbitrarily distributed maintenance time," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    5. Zheng, Yi-Xuan & Xiahou, Tangfan & Liu, Yu & Xie, Chaoyang, 2021. "Structure function learning of hierarchical multi-state systems with incomplete observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Zhou, Kai-Li & Cheng, De-Jun & Zhang, Han-Bing & Hu, Zhong-tai & Zhang, Chun-Yan, 2023. "Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Peng, Rui & Xiao, Hui & Guo, Jianjun & Lin, Chen, 2020. "Optimal defense of a distributed data storage system against hackers’ attacks," Reliability Engineering and System Safety, Elsevier, vol. 197(C).

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