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AZTEC: anticipatory capacity allocation for zero-touch network slicing

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de InvestigaciĂłn: Network Technologieses
dc.contributor.authorBega, Dario
dc.contributor.authorGramaglia, Marco
dc.contributor.authorFiore, Marco
dc.contributor.authorBanchs Roca, Albert
dc.contributor.authorCosta-PĂ©rez, Xavier
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2020-08-11T12:15:41Z
dc.date.available2020-08-11T12:15:41Z
dc.date.issued2020-08-04
dc.descriptionProceeding of: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications 6-9 July 2020 (Virtual Conference)en
dc.description.abstractThe combination of network softwarization with network slicing enables the provisioning of very diverse services over the same network infrastructure. However, it also creates a complex environment where the orchestration of network resources cannot be guided by traditional, human-in-the-loop network management approaches. New solutions that perform these tasks automatically and in advance are needed, paving the way to zero-touch network slicing.In this paper, we propose AZTEC, a data-driven framework that effectively allocates capacity to individual slices by adopting an original multi-timescale forecasting model. Hinging on a combination of Deep Learning architectures and a traditional optimization algorithm, AZTEC anticipates resource assignments that minimize the comprehensive management costs induced by resource overprovisioning, instantiation and reconfiguration, as well as by denied traffic demands.Experiments with real-world mobile data traffic show that AZTEC dynamically adapts to traffic fluctuations, and largely outperforms state-of-the-art solutions for network resource orchestration.en
dc.description.sponsorshipThe work of University Carlos III of Madrid was supported by H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5GROWTH project (grant agreement no. 856709). The research of M. Fiore was partially supported by ANR CANCAN project (ANR-18-CE25-0011).en
dc.format.extent10es
dc.identifier.bibliographicCitationIEEE INFOCOM 2020 - IEEE Conference on Computer Communications 6-9 July 2020 (Virtual Conference). IEEE, 2020, Pp. 794-803en
dc.identifier.doihttps://doi.org/10.1109/INFOCOM41043.2020.9155299
dc.identifier.isbn978-1-7281-6412-0
dc.identifier.publicationfirstpage794es
dc.identifier.publicationlastpage803es
dc.identifier.publicationtitleIEEE INFOCOM 2020 - IEEE Conference on Computer Communications 6-9 July 2020 (Virtual Conference)en
dc.identifier.urihttps://hdl.handle.net/10016/30757
dc.identifier.uxxiCC/0000030207
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate2020-07-06es
dc.relation.eventplaceVirtual conferenceen
dc.relation.eventtitleIEEE Conference on Computer Communications-IEEE INFOCOM 2020 (Virtual conference)en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/856950-5G-TOURSen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/856709-5GROWTHen
dc.rights© 2020 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherResource managementen
dc.subject.otherNetwork slicingen
dc.subject.otherForecastingen
dc.subject.otherEconomicsen
dc.subject.otherElectronic mailen
dc.subject.otherPredictive modelsen
dc.subject.otherCloud computingen
dc.titleAZTEC: anticipatory capacity allocation for zero-touch network slicingen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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