Abstract
In order to solve the problem of low accuracy of fault diagnosis algorithms brought by network dynamics, this paper proposes a fault diagnosis algorithm based on service characteristics under software defined network slicing. In order to reduce the problem of inaccurate symptom information caused by network dynamics, the credibility of symptoms is calculated based on the alternative probabilistic characteristics of network nodes, and the symptom information is corrected. The node importance is analyzed from the two dimensions of node centrality and number of links. Based on the node importance and symptom information, the reliability of the node failure is ranked. Finally, based on the maximum coverage algorithm, the optimal set of suspected faults is selected from the set of suspected faults as the final set of faults. The experiment compares the algorithm in this paper with the existing algorithm, and verifies that the algorithm in this paper effectively improves the accuracy of fault diagnosis.
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References
Kaizhi, H., Qirun, P., Quan, Y., et al.: A virtual node migration method for side channel risk perception. J. Electron. Inf. Sci. 41(9), 2164–2171 (2019)
Wu, B., Ho, P.H., Tapolcai, J., et al.: Optimal allocation of monitoring trails for fast SRLG failure localization in all-optical networks. In: Proceedings of 2010 IEEE Global Telecommunications Conference, Miami, USA, pp. 1–5 (2010)
Dusia, A., Sethi, A.S.: Recent advances in fault localization in computer networks. IEEE Commun. Surv. Tutor. 18(4), 3030–3051 (2016)
Rish, I., Brodie, M., Ma, S., et al.: Adaptive diagnosis in distributed systems. IEEE Trans. Neural Netw. 16(5), 1088–1109 (2005)
Jin, R., Wang, B., Wei, W., et al.: Detecting node failures in mobile wireless networks: a probabilistic approach. IEEE Trans. Mob. Comput. 15(7), 1647–1660 (2016)
Yu, Y., et al.: Falcon: differential fault localization for SDN control plane. Comput. Netw. 162(106851), 1–15 (2019)
Zegura, E.W., Calvert, K.L., Bhattacharjee, S.: How to model an internetwork. In: Proceedings of IEEE INFOCOM (1996)
Yu, M., Yi, Y., Rexford, J., Chiang, M.: Rethinking virtual network embedding: substrate support for path splitting and migration. ACM SIGCOMM CCR 38(2), 17–29 (2008)
Acknowledgement
This work is supported by science and technology project from State Grid Jiangsu Electric Power Co., Ltd: “Technology Research for High-efficiency and Intelligent Cooperative Wide-area Power Data Communication Networks (SGJSXT00DDJS1900168)”.
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Li, W., Cai, H., Jiang, C., Xia, P., Jiang, S., Lin, P. (2021). Fault Diagnosis Algorithm Based on Service Characteristics Under Software Defined Network Slicing. In: Cheng, M., Yu, P., Hong, Y., Jia, H. (eds) Smart Grid and Innovative Frontiers in Telecommunications. SmartGIFT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 373. Springer, Cham. https://doi.org/10.1007/978-3-030-73562-3_6
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DOI: https://doi.org/10.1007/978-3-030-73562-3_6
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