Pasteris, S;
Lin, Y;
He, T;
Wang, S;
Chan, K;
(2019)
Multicast-based Weight Inference in General Network Topologies.
In:
Proceedings of the IEEE ICC (International Conference on Communications) 2019.
IEEE: Shanghai, China.
(In press).
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Abstract
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-theart network tomography algorithm, and increasing the width of multicast substantially improves the inference accuracy.
| Type: | Proceedings paper |
|---|---|
| Title: | Multicast-based Weight Inference in General Network Topologies |
| Event: | IEEE ICC (International Conference on Communications) 2019 |
| Location: | Shanghai, China |
| Dates: | 20 May 2019 - 24 May 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://icc2019.ieee-icc.org/ |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10076416 |
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