Fast probabilistic fault diagnosis for large scale system
The challenges of fast fault diagnosis for large scale service systems are analyzed in this paper. A multi-layer management model is proposed to model the service scenario, which builds bipartite Bayesian network to denote dependence relationships. An incremental fault belief assessment method is proposed to analyze symptoms and compute posterior fault probabilities in an event-driven manner. Based on the method, we propose a greedy fault diagnosis algorithm to produce a sub-optimal explanation. To reduce the complexity of fault selection, we transform the fault diagnosis problem of finding MPE into finding most likely assignment of each fault, and propose corresponding algorithm. Simulation results prove the validity and efficiency of our algorithms.
dependency model event-driven fault diagnosis probabilistic diagnosis
L.W. Chu S.H. Zou S.D. Cheng W.D. Wang C.Q. Tian
State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecomm Department of Computer Science and Technology Tongji University Shanghai, China
国际会议
北京
英文
1430-1434
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）