An Improved Topology Inference Algorithm Based on End-to-End Measurements

Network topology inference, one of the studies of the network tomography, is the proposition of the network link-level performance inference. MLE and grouping methods recently have been proposed as means to infer network logical topology. The time spent on MLE increased sharply with the size of network, which may restrict the technique to be used in practice. The grouping method with less computation may lead to great errors because of the use of fixed threshold. In order to improve the disadvantages of the grouping method, this paper proposes an improved algorithm based on the grouping method, which dynamically adapts the threshold according to the estimation of link loss rate. The simulation results show that the improved algorithm has greater performance.