Accurate and effective inference of network link loss from unicast end-to-end measurements

Abstract

Understanding the network link loss is particularly important for optimising delay-sensitive applications. This study addresses the issue of estimating temporal dependence characteristic of link loss by using network tomography. Different from existing works of network loss tomography, the authors use a kth order Markov Chain (k-MC for short, k>1) to model the packet loss process, and propose a constrained optimisation-based method to estimate the state transmission probabilities of the k-MC link loss model. The authors also propose a top–down algorithm in order to ensure that our method can be applied to large networks. Compared with existing loss tomography methods, our method is capable of obtaining more accurate packet loss probability estimates. The ns-2 simulation results show the good performance of our method.