Link Delay Estimation Using Sparse Recovery for Dynamic Network Tomography

• Main Authors: Wei, Hao-Ting, Hsieh, Sung-Hsien, Hwang, Wen-Liang, Liao, Chung-Shou, Lu, Chun-Shien
• Format: Journal Article
• Language: English
• Published: 02.12.2018
• Subjects: Computer Science - Data Structures and Algorithms; Computer Science - Networking and Internet Architecture
• DOI: 10.48550/arxiv.1812.00369

When the scale of communication networks has been growing rapidly in the past decades, it becomes a critical challenge to extract fast and accurate estimation of key state parameters of network links, e.g., transmission delays and dropped packet rates, because such monitoring operations are usually time-consuming. Based on the sparse recovery technique reported in [Wang et al. (2015) IEEE Trans. Information Theory, 61(2):1028--1044], which can infer link delays from a limited number of measurements using compressed sensing, we particularly extend to networks with dynamic changes including link insertion and deletion. Moreover, we propose a more efficient algorithm with a better theoretical upper bound. The experimental result also demonstrates that our algorithm outperforms the previous work in running time while maintaining a similar recovery performance, which shows its capability to cope with large-scale networks.