Failure Localization through Progressive Network Tomography

Boolean Network Tomography (BNT) allows to localize network failures by means of end-to-end monitoring paths. Nevertheless, it falls short of providing efficient failure identification in real scenarios, due to the large combinatorial size of the solution space, especially when multiple failures occur concurrently. We aim at maximizing the identification capabilities of a bounded number of monitoring probes. To tackle this problem we propose a progressive approach to failure localization based on stochastic optimization, whose solution is the optimal sequence of monitoring paths to probe. We address the complexity of the problem by proposing a greedy strategy in two variants: one considers exact calculation of posterior probabilities of node failures given the observation, whereas the other approximates these values through a novel failure centrality metric. We discuss the approximation of the proposed approaches. Then, by means of numerical experiments conducted on real network topologies, we demonstrate the practical applicability of our approach. The performance evaluation evidences the superiority of our algorithms with respect to state of the art solutions based on classic Boolean Network Tomography as well as approaches based on sequential group testing.