An efficient solution to locate sparsely congested links by network tomography

Abstract

Locating individual congested links in large scale networks is an important but difficult problem, because of the hardness to directly measure the massive links. Current advantages of network tomography propose to infer the link congestion states by end-to-end measurements via solving a set of linear equations in Boolean algebra. But one challenging problem in such approaches is the requirement to construct n linearly independent measurements for uniquely identifying the states of n links. It is especially cost inefficient when the congested links are sparse, but requiring larger than n measurements to form a full-rank observation matrix. In this paper, we focus on efficient methods to take only limited number of path measurements to locate the sparsely congested links. To avoid the ambiguity of solving the boolean equations, at first, we propose a compressive sensing method to estimate the congestion probabilities of the individual links based on the deficient measurements (routing matrix is not full rank). Based on the congestion probability estimation, a greedy iterative estimation algorithm is developed to locate the congested links by online snapshot of the deficient measurements. Extensive simulations shows the effectiveness of proposed methods which reduce the measurement costs while preserving the detection accuracy.