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Intelligent failure localization and maintenance of network based on reliability

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Abstract

The network maintenance and failure localization are particularly important to ensure the high reliability of network operation. In this work, we propose one importance indicator based on reliability theory to measure the importance of each link and design the corresponding Monte Carlo algorithm. Meanwhile, we adopt intelligent BP neural network to deal with the problem of failure localization by predicting the failure probability of each link. Simulations indicate that the approach can achieve a high localization accuracy and reduce the use of monitoring equipment effectively. Whether the failed link located is worth maintaining is determined by its importance indicator. The proposed approach can be used by service providers to reduce the cost on network failure localization and maintenance as well as maintain the high reliable operation of network. As the approach is not restricted to specific network technologies, it can be widely applied to different network types.

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Data Availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Information and Computing Science, School of Mathematics, East China University of Science and Technology, Shanghai, 200237, China

    Qing Zheng & Fangming Shao

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  1. Qing Zheng
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  2. Fangming Shao
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Correspondence to Fangming Shao.

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Zheng, Q., Shao, F. Intelligent failure localization and maintenance of network based on reliability. J Supercomput 79, 389–418 (2023). https://doi.org/10.1007/s11227-022-04653-7

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