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An automated method for estimating reliability of grid systems using Bayesian networks

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  • Doguc, Ozge
  • Emmanuel Ramirez-Marquez, Jose

Abstract

Grid computing has become relevant due to its applications to large-scale resource sharing, wide-area information transfer, and multi-institutional collaborating. In general, in grid computing a service requests the use of a set of resources, available in a grid, to complete certain tasks. Although analysis tools and techniques for these types of systems have been studied, grid reliability analysis is generally computation-intensive to obtain due to the complexity of the system. Moreover, conventional reliability models have some common assumptions that cannot be applied to the grid systems. Therefore, new analytical methods are needed for effective and accurate assessment of grid reliability. This study presents a new method for estimating grid service reliability, which does not require prior knowledge about the grid system structure unlike the previous studies. Moreover, the proposed method does not rely on any assumptions about the link and node failure rates. This approach is based on a data-mining algorithm, the K2, to discover the grid system structure from raw historical system data, that allows to find minimum resource spanning trees (MRST) within the grid then, uses Bayesian networks (BN) to model the MRST and estimate grid service reliability.

Suggested Citation

  • Doguc, Ozge & Emmanuel Ramirez-Marquez, Jose, 2012. "An automated method for estimating reliability of grid systems using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 96-105.
    • Handle: RePEc:eee:reensy:v:104:y:2012:i:c:p:96-105
    DOI: 10.1016/j.ress.2012.03.016
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    References listed on IDEAS

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    1. Langseth, Helge & Portinale, Luigi, 2007. "Bayesian networks in reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 92-108.
    2. Dai, Yuan-Shun & Wang, Xiao-Long, 2006. "Optimal resource allocation on grid systems for maximizing service reliability using a genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1071-1082.
    3. Doguc, Ozge & Ramirez-Marquez, Jose Emmanuel, 2009. "A generic method for estimating system reliability using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 542-550.
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    Cited by:

    1. Lu, Lu & Xu, Zhengguo & Wang, Wenhai & Sun, Youxian, 2013. "A new fault detection method for computer networks," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 45-51.
    2. Tuballa, Maria Lorena & Abundo, Michael Lochinvar, 2016. "A review of the development of Smart Grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 710-725.
    3. Cai, Baoping & Liu, Yu & Fan, Qian, 2016. "A multiphase dynamic Bayesian networks methodology for the determination of safety integrity levels," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 105-115.
    4. Kamyab, Shahabeddin & Nematollahi, Mohammadreza & Henneaux, Pierre & Labeau, Pierre-Etienne, 2021. "Development of a hybrid method to assess grid-related LOOP scenarios for an NPP," Reliability Engineering and System Safety, Elsevier, vol. 206(C).

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