Matzner, Robin;
Luo, Ruijie;
Zervas, Georgios;
Bayvel, Polina;
(2023)
Intelligent performance inference: A graph neural network approach to modeling maximum achievable throughput in optical networks.
APL Machine Learning
, 1
(2)
, Article 026112. 10.1063/5.0137426.
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Abstract
One of the key performance metrics for optical networks is the maximum achievable throughput for a given network. Determining it, however, is a nondeterministic polynomial time (NP) hard optimization problem, often solved via computationally expensive integer linear programming (ILP) formulations. These are infeasible to implement as objectives, even on very small node scales of a few tens of nodes. Alternatively, heuristics are used although these, too, require considerable computation time for a large number of networks. There is, thus, a need for an ultra-fast and accurate performance evaluation of optical networks. For the first time, we propose the use of a geometric deep learning model, message passing neural networks (MPNNs), to learn the relationship between node and edge features, the network structure, and the maximum achievable network throughput. We demonstrate that MPNNs can accurately predict the maximum achievable throughput while reducing the computational time by up to five-orders of magnitude compared to the ILP for small networks (10–15 nodes) and compared to a heuristic for large networks (25–100 nodes)—proving their suitability for the design and optimization of optical networks on different time- and distance-scales.
Type: | Article |
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Title: | Intelligent performance inference: A graph neural network approach to modeling maximum achievable throughput in optical networks |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1063/5.0137426 |
Publisher version: | http://doi.org/10.1063/5.0137426 |
Language: | English |
Additional information: | © 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
UCL classification: | UCL 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 Electronic and Electrical Eng |
URI: | https://discovery.ucl.ac.uk/id/eprint/10170619 |
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