Link loss inference with link independence and nonlinear programming

We address the problem of inferring the network link loss rates using end-to-end measurements, which can also be formulated as network tomography. As we have known that most tomography problems are rank-deficit. One kind of method uses multiple probe measurements to acquire more information about the system that may generate much additional overhead; the other method imposes unrealistic assumption on the system. To address the issue that most network tomography methods cannot take into account both accuracy and efficiency, a novel link loss rate inference algorithm is proposed. In this paper, we get all identifiable links and then we utilize the information of these determined links to acquire the global distribution of the system. Moreover we partition all links in the network into several subsets. For each group, nonlinear programming is used to get the optimization solution of link loss rate. Finally, we evaluate our method and two former representative methods by the simulation. The results demonstrate that our method not only reduces the probe costs and the running time to a low level, but also makes a great improvement on the accuracy. Furthermore, our method can also perform well in more congested and large networks.