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

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dc.contributor.author Martín Pérez, Jorge
dc.contributor.author Kondepu, Koteswararao
dc.contributor.author De Vleeschauwer, Danny
dc.contributor.author Reddy, Venkatarami
dc.contributor.author Magalhaes Guimaraes, Carlos Eduardo
dc.contributor.author Sgambelluri, Andrea
dc.contributor.author Valcarenghi, Luca
dc.contributor.author Papagianni, Chrysa
dc.contributor.author Bernardos Cano, Carlos Jesús
dc.date.accessioned 2022-01-20T09:10:04Z
dc.date.available 2022-01-20T09:10:04Z
dc.date.issued 2022-01-12
dc.identifier.bibliographicCitation Martín, 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.
dc.identifier.issn 2169-3536
dc.identifier.uri http://hdl.handle.net/10016/33918
dc.description.abstract With 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.
dc.description.sponsorship Work partially funded by the EU H2020 5GROWTH Project (grant no. 856709) and H2020 collaborative Europe/Taiwan research project 5G-DIVE (grant no. 859881).
dc.format.extent 16
dc.language.iso eng
dc.publisher IEEE
dc.rights © The authors, 2022. This work is licensed under a Creative Commons Attribution 4.0 License.
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Vehicle-to-network
dc.subject.other Scaling
dc.subject.other Forecasting
dc.subject.other Time-series
dc.subject.other Machine learning
dc.title Dimensioning V2N services in 5G networks through forecast-based scaling
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/ACCESS.2022.3142346
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/856709
dc.relation.projectID info:eu-repo/grantAgreement/EC/GA-859881
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 0000
dc.identifier.publicationlastpage 0000
dc.identifier.publicationtitle IEEE Access
dc.identifier.publicationvolume 00
dc.identifier.uxxi AR/0000029313
dc.contributor.funder European Commission
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