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基于贝叶斯模型的高效主动探测算法(英文)

Active Probing Based Method for Fault Diagnosis Using Bayesian Network

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【作者】 乔焰邱雪松成璐孟洛明

【Author】 Qiao Yan,Qiu Xuesong,Cheng Lu,Meng Luoming State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications, Beijing 100876,P.R.China

【机构】 State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications

【摘要】 Fault diagnosis on large-scale and complex networks is a challenging task, as it requires efficient and accurate inference from huge data volumes. Active probing is a cost-efficient tool for fault diagnosis. However almost all existing probing-based techniques face the following problems: 1) performing inaccurately in noisy networks; 2) generating additional traffic to the network; 3) high cost computation. To address these problems, we propose an efficient probe selection algorithm for fault diagnosis based on Bayesian network. Moreover, two approaches which could significantly reduce the computational complexity of the probe selection process are provided. Finally, we implement the new proposed algorithm and a former representative probing-based algorithm (BPEA algorithm) on different settings of networks. The results show that, the new algorithm performs much faster than BPEA does without sacrificing the diagnostic quality, especially in large, noisy and multiple-fault networks.

【Abstract】 Fault diagnosis on large-scale and complex networks is a challenging task, as it requires efficient and accurate inference from huge data volumes. Active probing is a cost-efficient tool for fault diagnosis. However almost all existing probing-based techniques face the following problems: 1) performing inaccurately in noisy networks; 2) generating additional traffic to the network; 3) high cost computation. To address these problems, we propose an efficient probe selection algorithm for fault diagnosis based on Bayesian network. Moreover, two approaches which could significantly reduce the computational complexity of the probe selection process are provided. Finally, we implement the new proposed algorithm and a former representative probing-based algorithm (BPEA algorithm) on different settings of networks. The results show that, the new algorithm performs much faster than BPEA does without sacrificing the diagnostic quality, especially in large, noisy and multiple-fault networks.

【基金】 supported by National Key Basic Research Program of China (973 program) under Grant No.2007CB310703;Funds for Creative Research Groups of China under Grant No.60821001;National Natural Science Foundation of China under Grant No. 60973108;National S&T Major Project under Grant No.2011ZX03005-004-02
  • 【文献出处】 中国通信 ,China Communications , 编辑部邮箱 ,2011年07期
  • 【分类号】TP393.06;TP18
  • 【被引频次】5
  • 【下载频次】62
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