RouteNet-Erlang: A graph neural network for network performance evaluation
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
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ProjectINVESTIGACION EN FUTURAS REDES TOTALMENTE OPTIMIZADAS MEDIANTE INTELIGENCIA ARTIFICIAL - A (AEI-PID2020-118011GB-C21)
Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In the field of Deep Learning, Graph Neural Networks (GNN) have emerged as a new technique to build data-driven models that can learn complex and non-linear behavior. In this paper, we present RouteNet-Erlang, a pioneering GNN architecture designed to model computer networks. RouteNet-Erlang supports complex traffic models, multi-queue scheduling policies, routing policies and can provide accurate estimates in networks not seen in the training phase. We benchmark RouteNet-Erlang against a state-of-the-art QT model, and our results show that it outperforms QT in all the network scenarios.
CitationFerriol, M. [et al.]. RouteNet-Erlang: A graph neural network for network performance evaluation. A: Annual IEEE International Conference on Computer Communications. "IEEE INFOCOM 2022, IEEE Conference on Computer Communications: London, United Kingdom, May 2-5, 2022". Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 2018-2027. ISBN 978-1-6654-5822-1. DOI 10.1109/INFOCOM48880.2022.9796944.