Adaptive Path Selection for Link Loss Inference in Network Tomography Applications.

In this study, we address the problem of selecting the optimal end-to-end paths for link loss inference in order to improve the performance of network tomography applications, which infer the link loss rates from the path loss rates. Measuring the path loss rates using end-to-end probing packets may incur additional traffic overheads for networks, so it is important to select the minimum path set carefully while maximizing their performance. The usual approach is to select the maximum independent paths from the candidates simultaneously, while the other paths can be replaced by linear combinations of them. However, this approach ignores the fact that many paths always exist that do not lose any packets, and thus it is easy to determine that all of the links of these paths also have 0 loss rates. Not considering these good paths will inevitably lead to inefficiency and high probing costs. Thus, we propose an adaptive path selection method that selects paths sequentially based on the loss rates of previously selected paths. We also propose a theorem as well as a graph construction and decomposition approach to efficiently find the most valuable path during each round of selection. Our new method significantly outperforms the classical path selection method based on simulations in terms of the probing cost, number of accurate links determined, and the running speed.