Predictive fault management in the dynamic environment of IP networks

The growing complexity of IP networks in terms of hardware components, operating system, communication and application software and the huge amount of dependencies among them have caused an increase in demand for network management systems, particularly in fault management An efficient fault detection system needs to work effectively even in face of incomplete management information, uncertain situations and dynamic changes. In this paper, dynamic Bayesian networks are proposed to model static and dynamic dependencies between managed objects in IP networks. Prediction strategies and a backward inference approach are provided for the proactive management in fault detection based on the dynamic changes of IP networks.