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Artificial Intelligence for Elastic Management and Orchestration of 5G Networks

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Network Technologieses
dc.contributor.authorGutiérrez-Estévez, David M.
dc.contributor.authorGramaglia, Marco
dc.contributor.authorDomenico, Antonio de
dc.contributor.authorDandachi, Ghina
dc.contributor.authorKhatibi, Sina
dc.contributor.authorTsolkas, Dimitris
dc.contributor.authorBalan, Irina
dc.contributor.authorGarcía-Saavedra, Andrés
dc.contributor.authorElzur, Uri
dc.contributor.authorWang, Yue
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2019-11-21T13:38:12Z
dc.date.available2019-11-21T13:38:12Z
dc.date.issued2019-07
dc.description.abstractThe emergence of 5G enables a broad set of diversified and heterogeneous services with complex and potentially conflicting demands. For networks to be able to satisfy those needs, a flexible, adaptable, and programmable architecture based on network slicing is being proposed. A softwarization and cloudification of the communications networks is required, where network functions (NFs) are being transformed from programs running on dedicated hardware platforms to programs running over a shared pool of computational and communication resources. This architectural framework allows the introduction of resource elasticity as a key means to make an efficient use of the computational resources of 5G systems, but adds challenges related to resource sharing and efficiency. In this article, we propose Artificial Intelligence (AI) as a built-in architectural feature that allows the exploitation of the resource elasticity of a 5G network. Building on the work of the recently formed Experiential Network Intelligence (ENI) industry specification group of the European Telecommunications Standards Institute (ETSI) to embed an AI engine in the network, we describe a novel taxonomy for learning mechanisms that target exploiting the elasticity of the network as well as three different resource elastic use cases leveraging AI. This work describes the basis of a use case recently approved at ETSI ENI.en
dc.description.sponsorshipPart of this work has been performed within the 5G-MoNArch project (Grant Agreement No. 761445), part of the Phase II of the 5th Generation Public Private Partnership (5G-PPP) program partially funded by the European Commission within the Horizon 2020 Framework Program. This work was also supported by the the 5G-Transformer project (Grant Agreement No. 761536).en
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dc.identifier.bibliographicCitationIEEE Wireless Communications, 26(5), pp. 134-141en
dc.identifier.doihttps://doi.org/10.1109/MWC.2019.1800498
dc.identifier.issn1536-1284
dc.identifier.publicationfirstpage134
dc.identifier.publicationlastpage141
dc.identifier.publicationtitleIEEE Wireless Communicationsen
dc.identifier.urihttps://hdl.handle.net/10016/28824
dc.identifier.uxxiAR/0000023925
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/761445en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/761536en
dc.rights© 2019 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherResource elasticityen
dc.subject.otherArtificial intelligenceen
dc.subject.otherNetwork orchestrationen
dc.subject.otherSlice lifecycle management ETSI ENIen
dc.titleArtificial Intelligence for Elastic Management and Orchestration of 5G Networksen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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