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Addressing RouteNet scalability through input and output design
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Authors: Junior Momo Ziazet, Charles Boudreau, Brigitte Jaumard, Huy Duong
Status: Final
Date of publication: 22 September 2022
Published in: ITU Journal on Future and Evolving Technologies, Volume 3 (2022), Issue 2, Pages 224-234
Article DOI : https://doi.org/10.52953/GIOD4389
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Abstract:
With recent advances in the field of Machine Learning (ML), a multitude of problems related to communication systems and networks can be solved with data-driven solutions. Since data in these systems is mostly represented as graphs, Graph-based Neural Networks (GNNs) are a good candidate for solving such problems. These GNNs can be used as a computer network modeling technique to build models that accurately estimate the Key Performance Indicators (KPI) such as delay or jitter in real network scenarios in order to ensure their requirements in terms of service assurance. To build GNN solutions with higher accuracy, low computational resource requirements, and easy deployment of synthetic network training results into real-world networks, it is more than necessary to develop efficient and effective GNN models. This paper presents a GNN model capable of accurately estimating the average delay per flow in networks. By designing scale-independent features and using notions from queuing theory, the proposed model successfully generalizes to large size topologies, routing configurations, and traffic matrices not seen during the training phase.
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Keywords: 5G networks, graph neural network, KPI prediction, latency, network performance
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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