Link State Protocol Data Mining for Shared Risk Link Group Detection

In this paper, we use machine learning technique at the routers to study the link state protocol data to predict the existence of shared risk link groups (SRLG) in the network. In particular, we use the correlation between different link state updates (LSUs) issued by different network nodes (routers) upon failure. The concerned network router then runs a novel Bayesian network based statistical learning process to learn about the possible existence of SRLGs. The decision of this online learning is transferred to the routing information base (RIB) so that it can accordingly modify the routing table for the entire SRLG upon failure detection of one of the candidate node of that particular SRLG and hence reduce the protection switching time.