Static and Dynamic Failure Localization through Progressive Network Tomography

TL;DR

This paper introduces a progressive network tomography method for failure localization that minimizes probes in static scenarios and adapts to dynamic node failures, demonstrating improved efficiency over existing techniques.

Contribution

It proposes a novel progressive approach using stochastic optimization and greedy strategies for static and dynamic failure localization in networks.

Findings

Outperforms state-of-the-art Boolean Network Tomography methods.

Effective in real network topologies through numerical experiments.

Reduces the number of probes needed for failure identification.

Abstract

We aim at assessing the states of the nodes in a network by means of end-to-end monitoring paths. The contribution of this paper is twofold. First, we consider a static failure scenario. In this context, we aim at minimizing the number of probes to obtain failure identification. To face 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 by means of a novel failure centrality metric. Secondly, we adapt these two strategies to a dynamic failure scenario where nodes states can change throughout a monitoring period. By means of numerical experiments conducted on real network topologies, we demonstrate the practical applicability of our approach. Our 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.