A Variant betweenness Centrality Approach towards Distributed Network Monitoring

As the development of Internet thrives, the network management has to be taken a serious look. This paper faces the dilemma of putting more monitors into the system and maintains the original settings at the same time. We research the adding mechanism and present a new algorithm for finding the critical locations for additional deployment in the network, in the context of traffic estimation with link weight change method. The algorithm is based on Apriori search method that combines with the link weight change algorithm, then tested with Between ness Centrality to form the candidate snapshots. We also develop the greedy algorithm with Group Between ness Centrality(GBC) involved for the purpose of comparing. The result shows that the new algorithm need less additional monitors than greedy algorithm.