Preferential Link Tomography: Monitor Assignment for Inferring Interesting Link Metrics

We study the problem of identifying additive and static link metrics of a set of interesting links in a communication network, by using end-to-end cycle-free path measurements among selected monitors. To uniquely identify the metrics of these interesting links, three questions should be addressed: monitor assignment (which nodes should serve as monitors), paths selection (which cycle-free paths connecting each pair of monitors will be used), and link metric calculation. Since assigning a node as a monitor usually requires non-negligible operational cost, we focus on assigning the minimum number of monitors (i.e., Optimal monitor assignment) to identify all interesting links. By modeling the network as a connected graph, we propose Scalpel, an efficient preferential link tomography approach. Scalpel trims the original graph by a two-stage graph trimming algorithm and reuses existing method to assign monitors in the trimmed graph. We theoretically prove Scalpel has several key properties: 1) the graph trimming algorithm in Scalpel is minimal in the sense that further trimming the graph cannot reduce the number of monitors, 2) the obtained assignment is able to identify all interesting links in the original graph, and 3) an optimal monitor assignment in the graph after trimming is also an optimal monitor assignment in the original graph. Extensive simulations based on both synthetic topologies and real network topologies show the effectiveness of Scalpel. Compared with state-of-the-art, our approach reduces the number of monitors by 39.0%~98.6% when 50%~1% of all links are interesting links.