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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 Investigación: Network Technologieses
dc.contributor.authorSciancalepore, Vincenzo
dc.contributor.authorCosta Pérez, Xavier
dc.contributor.authorBanchs Roca, Albert
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2019-11-11T10:16:51Z
dc.date.available2019-11-11T10:16:51Z
dc.date.issued2019-07-18
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 Agreement 761536. The work of A. Banchs was supported in part by the 5GCity project of the Spanish Ministry of Economy and Competitiveness (TEC2016-76795-C6-3-R)en
dc.format.extent15
dc.identifier.bibliographicCitationSciancalepore, V., Costa- Pérez, X. y Banchs, A. (2019). RL-NSB: Reinforcement Learning-based 5G Network Slice Broker. IEEE Transactions on Networking, 27(4), pp. 1543 - 1557.en
dc.identifier.doihttps://doi.org/10.1109/TNET.2019.2924471
dc.identifier.issn1063-6692
dc.identifier.publicationfirstpage1543
dc.identifier.publicationissue27
dc.identifier.publicationlastpage1557
dc.identifier.publicationtitleIEEE Transactions on Networkingen
dc.identifier.publicationvolume4
dc.identifier.urihttps://hdl.handle.net/10016/29151
dc.identifier.uxxiAR/0000024022
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/761536en
dc.relation.projectIDGobierno de España. TEC2016-76795-C6-3-Res
dc.rights© 2019 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.other5Gen
dc.subject.otherWireless networksen
dc.subject.otherForecastingen
dc.subject.otherReinforcement learningen
dc.subject.otherVirtualizationen
dc.subject.otherNetwork slicingen
dc.titleRL-NSB: Reinforcement Learning-based 5G Network Slice Brokeren
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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