Matzner, R;
Luo, R;
Zervas, G;
Bayvel, P;
(2022)
Ultra-fast Optical Network Throughput Prediction using Graph Neural Networks.
In:
2022 International Conference on Optical Network Design and Modeling (ONDM).
IEEE: Warsaw, Poland.
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Abstract
One of the key performance metrics for optical networks is the maximum achievable throughput. Determining it however, is an NP-hard optimisation problem, often solved via computationally expensive integer linear programming (ILP) formulations. Heuristics, in conjunction with sequential loading, are scalable but non-exact. There is, thus, a need for ultra-fast performance evaluation of optical networks. For the first time, we propose message passing neural networks (MPNN), to learn the relationship between the structure and the maximum achievable throughput of optical networks. We demonstrate that MPNNs can accurately predict the maximum achievable throughput while reducing the computational time by 5-orders of magnitude compared to the ILP.
Type: | Proceedings paper |
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Title: | Ultra-fast Optical Network Throughput Prediction using Graph Neural Networks |
Event: | 2022 International Conference on Optical Network Design and Modeling (ONDM) |
Dates: | 16 May 2022 - 19 May 2022 |
ISBN-13: | 9783903176447 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.23919/ONDM54585.2022.9782853 |
Publisher version: | https://doi.org/10.23919/ONDM54585.2022.9782853 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Performance evaluation, Computational modeling, Message passing, Loading, Estimation, Optical fiber networks, Integer linear programming |
UCL classification: | 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 Electronic and Electrical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
URI: | https://discovery.ucl.ac.uk/id/eprint/10151258 |
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