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dc.contributor.authorFerriol Galmés, Miquel
dc.contributor.authorRusek, Krzysztof
dc.contributor.authorSuárez-Varela Maciá, José Rafael
dc.contributor.authorXiao, Shihan
dc.contributor.authorShi, Xiang
dc.contributor.authorCheng, Xiangle
dc.contributor.authorWu, Bo
dc.contributor.authorBarlet Ros, Pere
dc.contributor.authorCabellos Aparicio, Alberto
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2022-10-06T06:27:04Z
dc.date.available2022-10-06T06:27:04Z
dc.date.issued2022
dc.identifier.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.
dc.identifier.isbn978-1-6654-5822-1
dc.identifier.urihttp://hdl.handle.net/2117/374056
dc.description.abstractNetwork 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.
dc.description.sponsorshipThis publication is part of the Spanish I+D+i project TRAINER-A (ref.PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
dc.subject.lcshDeep learning
dc.subject.lcshGraph theory
dc.subject.lcshTelecommunication -- Traffic -- Management
dc.subject.otherNetwork modeling
dc.subject.otherGraph neural network
dc.titleRouteNet-Erlang: A graph neural network for network performance evaluation
dc.typeConference report
dc.subject.lemacAprenentatge profund
dc.subject.lemacGrafs, Teoria de
dc.subject.lemacTelecomunicació -- Tràfic -- Gestió
dc.contributor.groupUniversitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla
dc.identifier.doi10.1109/INFOCOM48880.2022.9796944
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9796944
dc.rights.accessOpen Access
local.identifier.drac33968018
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118011GB-C21/ES/INVESTIGACION EN FUTURAS REDES TOTALMENTE OPTIMIZADAS MEDIANTE INTELIGENCIA ARTIFICIAL - A/
local.citation.authorFerriol, M.; Rusek, K.; Suarez-varela, J.; Xiao, S.; Shi, X.; Cheng, X.; Wu, B.; Barlet, P.; Cabellos-Aparicio, A.
local.citation.contributorAnnual IEEE International Conference on Computer Communications
local.citation.publicationNameIEEE INFOCOM 2022, IEEE Conference on Computer Communications: London, United Kingdom, May 2-5, 2022
local.citation.startingPage2018
local.citation.endingPage2027


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