Multicast-Based Weight Inference in General Network Topologies

Yilei Lin, Ting He, Shiqiang Wang, Kevin Chan, Stephen Pasteris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Network topology plays an important role in many network operations. However, it is very difficult to obtain the topology of public networks due to the lack of internal cooperation. Network tomography provides a powerful solution that can infer the network routing topology from end-to-end measurements. Existing solutions all assume that routes from a single source form a tree. However, with the rapid deployment of Software Defined Networking (SDN) and Network Function Virtualization (NFV), the routing paths in modern networks are becoming more complex. To address this problem, we propose a novel inference problem, called the weight inference problem, which infers the finest-granularity information from end-to-end measurements on general routing paths in general topologies. Our measurements are based on emulated multicast probes with a controllable "width". We show that the problem has a unique solution when the multicast width is unconstrained; otherwise, we show that the problem can be treated as a sparse approximation problem, which allows us to apply variations of the pursuit algorithms. Simulations based on real network topologies show that our solution significantly outperforms a state-of-the-art network tomography algorithm, and increasing the width of multicast substantially improves the inference accuracy.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538680889
StatePublished - May 2019
Event2019 IEEE International Conference on Communications, ICC 2019 - Shanghai, China
Duration: May 20 2019May 24 2019

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2019 IEEE International Conference on Communications, ICC 2019

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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