End-to-end service failure diagnosis using belief networks

We present fault localization techniques suitable for diagnosing end-to-end service problems in communication systems with complex topologies. We refine a layered system model that represents relationships between services and functions offered between neighboring protocol layers. In a given layer, an end-to-end service between two hosts may be provided using multiple host-to-host services offered in this layer between two hosts on the end-to-end path. Relationships among end-to-end and host-to-host services form a bipartite probabilistic dependency graph whose structure depends on the network topology in the corresponding protocol layer. When an end-to-end service fails or experiences performance problems it is important to efficiently find the responsible host-to-host services. Finding the most probable explanation (MPE) of the observed symptoms is NP-hard. We propose two fault localization techniques based on Pearl's (1988) iterative algorithms for singly connected belief networks. The probabilistic dependency graph is transformed into a belief network, and then the approximations based on Pearl's algorithms and exact bucket tree elimination algorithm are designed and evaluated through extensive simulation study.