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

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dc.contributor.author Bega, Dario
dc.contributor.author Gramaglia, Marco
dc.contributor.author Fiore, Marco
dc.contributor.author Banchs Roca, Albert
dc.contributor.author Costa Perez, Xavier
dc.date.accessioned 2019-05-07T08:16:28Z
dc.date.available 2019-05-07T08:16:28Z
dc.date.issued 2019-06-17
dc.identifier.bibliographicCitation IEEE INFOCOM 2019- IEEE Conference on Computer Communications, 29 April-2 May 2019, Paris, France [proceedings], 9 pp.
dc.identifier.uri http://hdl.handle.net/10016/28331
dc.description Proceeding of: 2019 IEEE International Conference on Computer Communications (IEEE INFOCOM 2019), Paris (France), 29 April - 2 May, 2019.
dc.description.abstract Network 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.
dc.description.sponsorship The 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).
dc.format.extent 9
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2019 IEEE.
dc.subject.other Deep learning
dc.subject.other DeepCog
dc.subject.other Mobile traffic predictors
dc.subject.other Sliced 5G networks
dc.title DeepCog: cognitive network management in sliced 5G Networks with deep learning
dc.type conferenceObject
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/INFOCOM.2019.8737488
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grant/Agreeement/EC/H2020/761445/5G-MoNArch
dc.relation.projectID info:eu-repo/grant/Agreeement/EC/H2020/761445/5G-TRANSFORMER
dc.type.version submittedVersion
dc.relation.eventdate 2019, 29 April - 2 May
dc.relation.eventplace Paris (France)
dc.relation.eventtitle 2019 IEEE International Conference on Computer Communications (IEEE INFOCOM 2019)
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 1
dc.identifier.publicationlastpage 9
dc.identifier.uxxi CC/0000028525
dc.contributor.funder European Commission
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