Fast probabilistic fault diagnosis for large scale system

Abstract

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.