Topology Inference of Unknown Networks Based on Robust Virtual Coordinate Systems

Journal Article · 31 January 2019 · IEEE/ACM Transactions on Networking

DOI: https://doi.org/10.1109/tnet.2018.2888600 · OSTI ID:1526574

Bouchoucha, Taha  ^[1]; Chuah, Chen-Nee  ^[1]; Ding, Zhi  ^[1]

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1. Univ. of California, Davis, CA (United States). Dept. of Electrical and Computer Engineering

Learning and exploring the connectivity of unknown networks represent an important problem in practical applications of communication networks and social-media networks. Modeling large-scale networks as connected graphs is highly desirable to extract their connectivity information among nodes to visualize network topology, disseminate data, and improve routing efficiency. This paper investigates a simple measurement model in which a small subset of source nodes collect hop distance information from networked nodes in order to generate a virtual coordinate system (VCS) for networks of unknown topology. We establish the VCS to define logical distance among nodes based on principal component analysis and to determine connectivity relationship and effective routing methods. More importantly, we present a robust analytical algorithm to derive the VCS against practical issues of missing and corrupted measurements. We also develop a connectivity inference method which classifies nodes into layers based on the hop distances and derives partial information on network connectivity.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); National Science Foundation
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1526574
• Journal Information: IEEE/ACM Transactions on Networking, Journal Name: IEEE/ACM Transactions on Networking Journal Issue: 1 Vol. 27; ISSN 1063-6692
• Publisher: IEEE - ACMCopyright Statement
• Country of Publication: United States
• Language: English