An adaptive compressive sensing scheme for network tomography based fault localization

A scalable network fault localization scheme based on compressive sensing is proposed. Aimed at large networks, the proposed scheme monitors a network with a few paths covering the network, and upon detection of anomalies in one or more paths, adaptively carries out additional end-to-end measurements to localize the faulty links. Each adaptive measurement covers a set of links identified based on the previous resolution. The scheme is highly scalable as the total number of measurements required grows logarithmically with the number of links in the network - a level of scalability not practically achieved for network data inference with compressive sensing so far. The scheme is tested on realistic Internet topologies with Gilbert-Elliott loss model calibrated with measurements made on Planet-Lab infrastructure. Results indicate that the converged solution of the proposed scheme achieves over 99% detection rates and less than 1% false positive rates. The proposed scalable scheme is accurate in terms of detection, cost effective in terms of implementation, and casts a minimal monitoring traffic load.