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Quantifying the impact of correlated failures on system reliability by a simulation approach

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Listed:
  • Lin, Yi-Kuei
  • Fiondella, Lance
  • Chang, Ping-Chen

Abstract

Correlation poses a serious threat to many engineered systems because the simultaneous failure of multiple components can dangerously degrade performance. Given the high cost of system failures in business and mission-critical applications, methods to explicitly consider the impact of correlation on system reliability are essential. This paper constructs a stochastic-flow network model to analyze the performance of a computer network, where there exists correlation between the failures of all the physical lines and routers comprising the edges and nodes of the network. That is, we address global-scale events that can cause widespread damage to the performance of the network. We propose a simulation approach to estimate the probability that a given amount of data can be sent from a source to sink through this network. This probability that the network satisfies a specified level of demand is referred to as the system reliability. Experimental results demonstrate that correlation can produce a substantial impact on system reliability. The proposed approach, thus, captures the influence of correlation on system reliability and offers a method to quantify the utility of reducing correlation.

Suggested Citation

  • Lin, Yi-Kuei & Fiondella, Lance & Chang, Ping-Chen, 2013. "Quantifying the impact of correlated failures on system reliability by a simulation approach," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 32-40.
    • Handle: RePEc:eee:reensy:v:109:y:2013:i:c:p:32-40
    DOI: 10.1016/j.ress.2012.08.008
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    References listed on IDEAS

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    1. Lin, Yi-Kuei & Yeh, Cheng-Ta, 2011. "Maximal network reliability for a stochastic power transmission network," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1332-1339.
    2. Yi‐Kuei Lin & Ping‐Chen Chang, 2012. "Evaluation of system reliability for a cloud computing system with imperfect nodes," Systems Engineering, John Wiley & Sons, vol. 15(1), pages 83-94, March.
    3. Ramirez-Marquez, Jose Emmanuel & Coit, David W., 2007. "Multi-state component criticality analysis for reliability improvement in multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1608-1619.
    4. Yeh, Wei-Chang, 2008. "A simple minimal path method for estimating the weighted multi-commodity multistate unreliable networks reliability," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 125-136.
    5. Joseph C. Hudson & Kailash C. Kapur, 1985. "Reliability Bounds for Multistate Systems with Multistate Components," Operations Research, INFORMS, vol. 33(1), pages 153-160, February.
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    Citations

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    Cited by:

    1. Ping-Chen Chang, 2019. "Reliability estimation for a stochastic production system with finite buffer storage by a simulation approach," Annals of Operations Research, Springer, vol. 277(1), pages 119-133, June.
    2. Ping-Chen Chang, 2022. "Reliability evaluation and big data analytics architecture for a stochastic flow network with time attribute," Annals of Operations Research, Springer, vol. 311(1), pages 3-18, April.
    3. Aboalkhair, Ahmad M. & Coolen, Frank P.A. & MacPhee, Iain M., 2014. "Nonparametric predictive inference for reliability of a k-out-of-m:G system with multiple component types," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 298-304.
    4. Yi-Kuei Lin & Lance Fiondella & Ping-Chen Chang, 2022. "Reliability of time-constrained multi-state network susceptible to correlated component faults," Annals of Operations Research, Springer, vol. 311(1), pages 239-254, April.
    5. Bai, Guanghan & Zuo, Ming J. & Tian, Zhigang, 2015. "Search for all d-MPs for all d levels in multistate two-terminal networks," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 300-309.

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