Temporal Dependence Network Link Loss Inference from Unicast End-to-End Measurements

摘要

In this letter, we address the issue of estimating the temporal dependence characteristic of link loss by using network tomography. We use a k-th order Markov chain (k > 1) to model the packet loss process, and estimate the state transition probabilities of the link loss model using a constrained optimization-based method. Analytical and simulation results indicate that our method yields more accurate packet loss probability estimates than existing loss inference methods.