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𝛼-OMC: cost-aware deep learning for mobile network resource orchestration

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Network Technologieses
dc.contributor.authorBega, Dario
dc.contributor.authorGramaglia, Marco
dc.contributor.authorFiore, Marco
dc.contributor.authorBanchs Roca, Albert
dc.contributor.authorCosta-Pérez, Xavier
dc.date.accessioned2019-05-07T09:15:04Z
dc.date.available2019-05-07T09:15:04Z
dc.date.issued2019-09-23
dc.descriptionProceeding of: The 2nd International Workshop on Network Intelligence (NI 2019): Machine Learning for Networking (part of 2019 IEEE International Conference on Computer Communications (IEEE INFOCOM 2019)), 29 April, 2019, Paris, Franceen
dc.description.abstractOrchestrating resources in 5G and beyond-5G systems will be substantially more complex than it used to be in previous generations of mobile networks. In order to take full advantage of the unprecedented possibilities for dynamic reconfiguration offered by network softwarization and virtualization technologies, operators have to embed intelligence in network resource orchestrators. We advocate that the automated, data-driven decisions taken by orchestrators must be guided by considerations on the cost that such decisions involve for the operator. We show that such a strategy can be implemented via a deep learning architecture that forecasts capacity rather than plain traffic, thanks to a novel loss function named alfa-OMC. We investigate the convergence properties of alfa-OMC, and provide preliminary results on the performance of the learning process in case studies with real-world mobile network traffic.en
dc.description.sponsorshipThe work of University Carlos III of Madrid was supported by the H2020 5G-MoNArch project (Grant Agreement No. 761445) and the work of NEC Europe Ltd. by the 5GTransformer project (Grant Agreement No. 761536).en
dc.format.extent6
dc.identifier.bibliographicCitationIEEE INFOCOM 2019- IEEE Conference on Computer Communications, 29 April-2 May 2019, Paris, France [proceedings], 6 pp.en
dc.identifier.doihttps://doi.org/10.1109/INFCOMW.2019.8845178
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage6
dc.identifier.urihttps://hdl.handle.net/10016/28332
dc.identifier.uxxiCC/0000029120
dc.language.isoengen
dc.relation.eventdate2019, April 29en
dc.relation.eventnumber12
dc.relation.eventplaceParis (France)en
dc.relation.eventtitleInternational Workshop on Network Intelligence (NI 2019): Machine Learning for Networking (part of 2019 IEEE International Conference on Computer Communications (IEEE INFOCOM 2019))en
dc.relation.projectIDinfo:eu-repo/grant/Agreeement/EC/H2020/761445/5G-MoNArchen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/761536/5G Transformeren
dc.rights© 2019 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.other5G networksen
dc.subject.otherDeep learningen
dc.subject.otherMobile networksen
dc.title𝛼-OMC: cost-aware deep learning for mobile network resource orchestrationen
dc.title.alternativeAlfa-OMC: cost-aware deep learning for mobile network resource orchestrationen
dc.typeconference proceedings*
dc.type.hasVersionSMUR*
dspace.entity.typePublication
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