Scalable and fast root cause analysis using inter cluster inference

The capability to diagnose the root cause of an observed problem precisely and quickly is a desirable feature for large communication networks. However, the design of a technique that is at the same time fast, scalable and accurate is a challenging task. In this paper, we propose a novel method based on inter-cluster inference to overcome the usual limits of fault diagnosis techniques. The approach is based on two important concepts: a cluster decomposition of the dependency graph in order to ensure scalability, and the introduction of duplicated nodes aiming at preserving the end-to-end network view. The evaluation of the proposed approach has demonstrated a significant reduction in the complexity and the computation time of the root cause analysis, since it is based on a set of small-scale dependency graphs.