Fault Diagnosis for Large-Scale IP Networks Based on Dynamic Bayesian Model

To improve the quality of IP service, it is important to quickly and accurately diagnosis the root fault from the observed symptoms and knowledge. The approximate inference based on Bayesian networks is the most popular fault diagnosis technology in recent years. Presently, fault localization based on Bayesian networks is only according to the current information and does not consider the time information. The existing methods based on dynamic Bayesian networks are not fit for large-scale networks because of their complexity. This paper establishes a fault diagnosis model for large-scale IP networks based on dynamic Bayesian networks by improving a representative exact algorithm and implements simulation. The results show that the algorithm can run well. This method makes full use of the historical data and current observations to estimate the current system state and complete the fault diagnosis.