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DeepCog: cognitive network management in sliced 5G Networks with deep learning

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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.contributor.funderEuropean Commissiones
dc.date.accessioned2019-05-07T08:16:28Z
dc.date.available2019-05-07T08:16:28Z
dc.date.issued2019-06-17
dc.descriptionProceeding of: 2019 IEEE International Conference on Computer Communications (IEEE INFOCOM 2019), Paris (France), 29 April - 2 May, 2019.en
dc.description.abstractNetwork slicing is a new paradigm for future 5G networks where the network infrastructure is divided into slices devoted to different services and customized to their needs. With this paradigm, it is essential to allocate to each slice the needed resources, which requires the ability to forecast their respective demands. To this end, we present DeepCog, a novel data analytics tool for the cognitive management of resources in 5G systems. DeepCog forecasts the capacity needed to accommodate future traffic demands within individual network slices while accounting for the operator’s desired balance between resource overprovisioning (i.e., allocating resources exceeding the demand) and service request violations (i.e., allocating less resources than required). To achieve its objective, DeepCog hinges on a deep learning architecture that is explicitly designed for capacity forecasting. Comparative evaluations with real-world measurement data prove that DeepCog’s tight integration of machine learning into resource orchestration allows for substantial (50% or above) reduction of operating expenses with respect to resource allocation solutions based on state-of-theart mobile traffic predictors. Moreover, we leverage DeepCog to carry out an extensive first analysis of the trade-off between capacity overdimensioning and unserviced demands in adaptive, sliced networks and in presence of real-world 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 Laboratories Europe by the 5GTransformer project (Grant Agreement No. 761536). The work of CNR-IEIIT was partially supported by the ANR CANCAN project (ANR-18-CE25-0011).en
dc.format.extent9es
dc.identifier.bibliographicCitationIEEE INFOCOM 2019- IEEE Conference on Computer Communications, 29 April-2 May 2019, Paris, France [proceedings], 9 pp.en
dc.identifier.doihttps://doi.org/10.1109/INFOCOM.2019.8737488
dc.identifier.publicationfirstpage1es
dc.identifier.publicationlastpage9es
dc.identifier.urihttps://hdl.handle.net/10016/28331
dc.identifier.uxxiCC/0000028525
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate2019, 29 April - 2 Mayen
dc.relation.eventplaceParis (France)en
dc.relation.eventtitle2019 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/grant/Agreeement/EC/H2020/761445/5G-TRANSFORMERen
dc.rights© 2019 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherDeep learningen
dc.subject.otherDeepCogen
dc.subject.otherMobile traffic predictorsen
dc.subject.otherSliced 5G networksen
dc.titleDeepCog: cognitive network management in sliced 5G Networks with deep learningen
dc.typeconference proceedings*
dc.type.hasVersionSMUR*
dspace.entity.typePublication
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