Localization of Network Performance Problems with Multi-Level Discrete Tomography

The focus of network monitoring and problem diagnosis gradually moves towards "soft failures" and performance problems, such as noticeable jitter or loss rate. A major, and still unresolved, problem in this space is to localize a performance problem at the granularity of individual IP-layer links. We propose a network tomography framework that aims to localize performance problems in the set of paths that interconnect a full-mesh topology formed by N sensors. As opposed to Boolean tomography, which models links as "good" versus "bad", we consider a multi-level discrete tomography framework that can capture the large diversity of performance levels seen at the Internet. We evaluate two tomography methods: the first assumes that the performance of a path is determined by the lowest-performance link in the path; the second also takes into account the number of such links. We propose an efficient algorithm for each method and then evaluate their accuracy and also show their superiority over two traditional approaches (Boolean and Analogue) on real single-provider networks (ESnet and Internet2) as well as in an interdomain context (PlanetLab).