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RL-NSB: reinforcement learning-based 5G network slice broker

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de InvestigaciĂłn: Network Technologieses
dc.contributor.authorSciancalepore, Vincenzo
dc.contributor.authorCosta-PĂ©rez, Xavier
dc.contributor.authorBanchs Roca, Albert
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2023-08-28T07:22:22Z
dc.date.available2023-08-28T07:22:22Z
dc.date.issued2019-08-01
dc.description.abstractNetwork slicing is considered one of the main pillars of the upcoming 5G networks. Indeed, the ability to slice a mobile network and tailor each slice to the needs of the corresponding tenant is envisioned as a key enabler for the design of future networks. However, this novel paradigm opens up to new challenges, such as isolation between network slices, the allocation of resources across them, and the admission of resource requests by network slice tenants. In this paper, we address this problem by designing the following building blocks for supporting network slicing: i) traffic and user mobility analysis, ii) a learning and forecasting scheme per slice, iii) optimal admission control decisions based on spatial and traffic information, and iv) a reinforcement process to drive the system towards optimal states. In our framework, namely RL-NSB, infrastructure providers perform admission control considering the service level agreements (SLA) of the different tenants as well as their traffic usage and user distribution, and enhance the overall process by the means of learning and the reinforcement techniques that consider heterogeneous mobility and traffic models among diverse slices. Our results show that by relying on appropriately tuned forecasting schemes, our approach provides very substantial potential gains in terms of system utilization while meeting the tenants' SLAs.en
dc.description.sponsorshipThe work of V. Sciancalepore and X. Costa-Perez was supported by the European Union H-2020 Project 5G-TRANSFORMER under Grant 761536. The work of A. Banchs was supported in part by the 5GCity project of the Spanish Ministry of Economy and Competitiveness under Grant TEC2016-76795-C6- 3-Res
dc.identifier.bibliographicCitationSciancalepore, V., Costa- PĂ©rez, X. y Banchs, A. (2019). RL-NSB: Reinforcement Learning-based 5G Network Slice Broker. IEEE Transactions on Networking, 27(4), pp. 1543 - 1557en
dc.identifier.doihttps://doi.org/10.1109/TNET.2019.2924471
dc.identifier.issn1063-6692
dc.identifier.publicationfirstpage1543es
dc.identifier.publicationissue4es
dc.identifier.publicationlastpage1557es
dc.identifier.publicationtitleIEEE-ACM TRANSACTIONS ON NETWORKINGes
dc.identifier.publicationvolume27es
dc.identifier.urihttps://hdl.handle.net/10016/38109
dc.identifier.uxxiAR/0000024022
dc.language.isoenges
dc.publisherIEEEes
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/761536es
dc.relation.projectIDGobierno de España. TEC2016-76795-C6- 3-Res
dc.rights© IEEEes
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.other5ges
dc.subject.otherForecastingen
dc.subject.otherNetwork slicingen
dc.subject.otherReinforcement learningen
dc.subject.otherVirtualizationen
dc.subject.otherWireless networksen
dc.titleRL-NSB: reinforcement learning-based 5G network slice brokeren
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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