LAUSR

As a technique to investigate behaviours of a computer network with low operational cost, network tomography has received considerable attentions in recent years. Most studies in this area assume that the topology of the network of interest is known, and try to propose computationally and/or statistically efficient methods to estimate link-level properties such as loss rate, delay distribution, bandwidth etc., or global traffic properties such as point-to-point traffic matrix. Little progresses have been made for scenarios when topology of the target network is unknown, although it is often the case in many practical applications. The few published works for topology tomography resolved the problem primarily by clustering analysis, which works for tree-like networks only and often suffers from unstable performance for large networks of complicated structure. In this article, we study the classic problem of network tomography from a new perspective. By connecting the problem of topology tomography to the classic machine learning problem of “market basket analysis,” we find that simultaneous topology and loss tomography can be achieved by discovering association patterns of loss records collected at receivers, which can be efficiently resolved with light modifications of a recently developed statistical method known as the “theme dictionary model”. Both theoretical analysis and simulation studies demonstrate that the proposed approach enjoys improved effectiveness for networks of tree as well as general topology with slightly higher computational costs.