Efficient method of station selection for passive monitoring in distributed network using information gain

Network monitoring is essential for assessing performance issues, identifying and locating problems. There is increasing interest in passive monitoring of flows at multiple locations within a distributed network. In order to figure out how to place monitors under cost-effective and budget constraints within the network, a new approach is presented in this paper, which solves the problem of monitoring stations selection and the tradeoff between monitoring cost and reward. Using the method from combinatorial optimization on submodular of information gain, the approach firstly proves that joint entropy and information gain in network models satisfy submodularity under certain conditions, and then an approximate algorithm is proposed to solve optimizing problem of conditional entropies. On the basis, monitoring stations selected by our solution are much better than existing classical criterion. The simulation results validate the approach, demonstrating the solution improving monitoring quality, accuracy and computation time.