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Scalable k-out-of-n models for dependability analysis with Bayesian networks

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  • Bibartiu, Otto
  • Dürr, Frank
  • Rothermel, Kurt
  • Ottenwälder, Beate
  • Grau, Andreas

Abstract

Availability analysis is indispensable in evaluating the dependability of safety and business-critical systems, for which fault tree analysis (FTA) has proven very useful throughout research and industry. Fault trees (FT) can be analyzed by means of a rich set of mathematical models. One particular model are Bayesian networks (BNs) which have gained considerable popularity recently due to their powerful inference abilities. However, large-scale systems, as found in modern data centers for cloud computing, pose modeling challenges that require scalable availability models. An equivalent BN of a FT has no scalable representation for the k-out-of-n (k/n) voting gate because the conditional probability table that constitutes the k/n voting gate grows exponentially in n. Thus, the memory becomes the limiting factor.

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  • Bibartiu, Otto & Dürr, Frank & Rothermel, Kurt & Ottenwälder, Beate & Grau, Andreas, 2021. "Scalable k-out-of-n models for dependability analysis with Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:reensy:v:210:y:2021:i:c:s0951832021000910
    DOI: 10.1016/j.ress.2021.107533
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    References listed on IDEAS

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    1. Kammouh, Omar & Gardoni, Paolo & Cimellaro, Gian Paolo, 2020. "Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    2. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    3. Zhang, Y. & Weng, W.G., 2020. "Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    4. Tien, Iris & Der Kiureghian, Armen, 2016. "Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 134-147.
    5. Xiang, W. & Zhou, W., 2021. "Bayesian network model for predicting probability of third-party damage to underground pipelines and learning model parameters from incomplete datasets," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    6. Bensi, Michelle & Kiureghian, Armen Der & Straub, Daniel, 2013. "Efficient Bayesian network modeling of systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 200-213.
    7. Durga Rao, K. & Gopika, V. & Sanyasi Rao, V.V.S. & Kushwaha, H.S. & Verma, A.K. & Srividya, A., 2009. "Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 872-883.
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    Cited by:

    1. Yu, Yaocheng & Shuai, Bin & Huang, Wencheng, 2024. "Resilience evaluation of train control on-board system considering common cause failure: Based on a beta-factor and continuous-time bayesian network model," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    2. Wang, Fang & Bai, Jie & Liu, Linlin & Ye, Tianyuan, 2024. "Temporal noisy-adder of bayesian network for scalable consecutive-k-out-of-n:F system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Yin, Juan & Cui, Lirong & Sun, Yudao & Balakrishnan, Narayanaswamy, 2022. "Reliability modelling for linear and circular k-out-of-n: F systems with shared components," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Xiahou, Tangfan & Zheng, Yi-Xuan & Liu, Yu & Chen, Hong, 2023. "Reliability modeling of modular k-out-of-n systems with functional dependency: A case study of radar transmitter systems," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    5. Yılmaz, Emre & German, Brian J. & Pritchett, Amy R., 2023. "Optimizing resource allocations to improve system reliability via the propagation of statistical moments through fault trees," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Liu, Jin & Zhai, Changhai & Yu, Peng, 2022. "A Probabilistic Framework to Evaluate Seismic Resilience of Hospital Buildings Using Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

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