Scalpel: Scalable Preferential Link Tomography Based on Graph Trimming

Abstract

Inferring per-link metrics through aggregated path measurements, known as network tomography, is an effective way to facilitate various network operations, such as network monitoring, load balancing, and fault diagnosis. We study the problem of identifying additive link metrics of a set of interesting links from end-to-end cycle-free path measurements among selected monitors, i.e., preferential link tomography. 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, a scalable preferential link tomography approach. Scalpel trims the original graph by a two-stage graph trimming algorithm and reuses an 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 does not 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. We implement Scalpel and evaluate it based on both synthetic topologies and real network topologies. Compared with state-of-the-art, Scalpel reduces the number of monitors by 39.0% to 98.6% when 50% to 1% of all links are interesting links.