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Dimensioning V2N services in 5G networks through forecast-based scaling

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Network Technologieses
dc.contributor.authorMartín Pérez, Jorge
dc.contributor.authorKondepu, Koteswararao
dc.contributor.authorDe Vleeschauwer, Danny
dc.contributor.authorReddy, Venkatarami
dc.contributor.authorMagalhaes Guimaraes, Carlos Eduardo
dc.contributor.authorSgambelluri, Andrea
dc.contributor.authorValcarenghi, Luca
dc.contributor.authorPapagianni, Chrysa
dc.contributor.authorBernardos Cano, Carlos Jesús
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2022-01-20T09:10:04Z
dc.date.available2022-01-20T09:10:04Z
dc.date.issued2022-01-12
dc.description.abstractWith the increasing adoption of intelligent transportation systems and the upcoming era of autonomous vehicles, vehicular services (such as remote driving, cooperative awareness, and hazard warning) will have to operate in an ever-changing and dynamic environment. Anticipating the dynamics of traffic flows on the roads is critical for these services and, therefore, it is of paramount importance to forecast how they will evolve over time. By predicting future events (such as traffic jams) and demands, vehicular services can take proactive actions to minimize Service Level Agreement (SLA) violations and reduce the risk of accidents. In this paper, we compare several techniques, including both traditional time-series and recent Machine Learning (ML)-based approaches, to forecast the traffic flow at different road segments in the city of Torino (Italy). Using the the most accurate forecasting technique, we propose n-max algorithm as a forecast-based scaling algorithm for vertical scaling of edge resources, comparing its benefits against state-of-the-art solutions for three distinct Vehicle-to-Network (V2N) services. Results show that the proposed scaling algorithm outperforms the state-of-the-art, reducing Service Level Objective (SLO) violations for remote driving and hazard warning services.en
dc.description.sponsorshipWork partially funded by the EU H2020 5GROWTH Project (grant no. 856709) and H2020 collaborative Europe/Taiwan research project 5G-DIVE (grant no. 859881).en
dc.format.extent16
dc.identifier.bibliographicCitationMartín, J., Kondepu, K., de Vleeschaumer, D., Reddy, V., Guimaraes, C., Sgambelluri, A., Valcarengui, L., Papagianni, C. & Bernardos, C. J. (2022). Dimensioning V2N services in 5G networks through forecast-based scaling. IEEE Access, 00, 0000-0000.en
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2022.3142346
dc.identifier.issn2169-3536
dc.identifier.publicationfirstpage0000
dc.identifier.publicationlastpage0000
dc.identifier.publicationtitleIEEE Accessen
dc.identifier.publicationvolume00
dc.identifier.urihttps://hdl.handle.net/10016/33918
dc.identifier.uxxiAR/0000029313
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/856709
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/GA-859881
dc.rights© The authors, 2022. This work is licensed under a Creative Commons Attribution 4.0 License.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherVehicle-to-networken
dc.subject.otherScalingen
dc.subject.otherForecastingen
dc.subject.otherTime-seriesen
dc.subject.otherMachine learningen
dc.titleDimensioning V2N services in 5G networks through forecast-based scalingen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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