Smart Grid and Innovative Frontiers in Telecommunications. 5th EAI International Conference, SmartGIFT 2020, Chicago, USA, December 12, 2020, Proceedings

Research Article

Fault Diagnosis Algorithm Based on Service Characteristics Under Software Defined Network Slicing

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  • @INPROCEEDINGS{10.1007/978-3-030-73562-3_6,
        author={Wei Li and Hao Cai and Chunxia Jiang and Ping Xia and Song Jiang and Peng Lin},
        title={Fault Diagnosis Algorithm Based on Service Characteristics Under Software Defined Network Slicing},
        proceedings={Smart Grid and Innovative Frontiers in Telecommunications. 5th EAI International Conference, SmartGIFT 2020, Chicago, USA, December 12, 2020, Proceedings},
        proceedings_a={SMARTGIFT},
        year={2021},
        month={7},
        keywords={Software defined network Network slicing Fault diagnosis Network characteristics},
        doi={10.1007/978-3-030-73562-3_6}
    }
    
  • Wei Li
    Hao Cai
    Chunxia Jiang
    Ping Xia
    Song Jiang
    Peng Lin
    Year: 2021
    Fault Diagnosis Algorithm Based on Service Characteristics Under Software Defined Network Slicing
    SMARTGIFT
    Springer
    DOI: 10.1007/978-3-030-73562-3_6
Wei Li1, Hao Cai1, Chunxia Jiang1, Ping Xia1, Song Jiang1, Peng Lin2
  • 1: State Grid Jiangsu Electric Power Co., Ltd.
  • 2: Beijing Vectinfo Technologies Co., Ltd.

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.