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DeepCog: optimizing resource provisioning in network slicing with AI-based capacity forecasting

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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 Commissionen
dc.date.accessioned2020-02-24T16:22:50Z
dc.date.available2020-02-24T16:22:50Z
dc.date.issued2020-02
dc.description.abstractThe dynamic management of network resources is both a critical and challenging task in upcoming multi-tenant mobile networks, which requires allocating capacity to individual network slices so as to accommodate future time-varying service demands. Such an anticipatory resource configuration process must be driven by suitable predictors that take into account the monetary cost associated to overprovisioning or underprovisioning of networking capacity, computational power, memory, or storage. Legacy models that aim at forecasting traffic demands fail to capture these key economic aspects of network operation. To close this gap, we present DeepCog, a deep neural network architecture inspired by advances in image processing and trained via a dedicated loss function. Unlike traditional traffic volume predictors, DeepCog returns a cost-aware capacity forecast, which can be directly used by operators to take short- and long-term reallocation decisions that maximize their revenues. Extensive performance evaluations with real-world measurement data collected in a metropolitan-scale operational mobile network demonstrate the effectiveness of our proposed solution, which can reduce resource management costs by over 50% in practical case studies.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 5G-TRANSFORMER project (grant agree-ment no. 761536) and 5GROWTH project (grant agreement no. 856709).en
dc.format.extent15
dc.identifier.bibliographicCitationIEEE Journal on selected areas in communications, 38(2), Feb. 2020, Pp. 361-376en
dc.identifier.doihttps://doi.org/10.1109/JSAC.2019.2959245
dc.identifier.issn0733-8716
dc.identifier.issn1558-0008 (online)
dc.identifier.publicationfirstpage361
dc.identifier.publicationissue2
dc.identifier.publicationlastpage376
dc.identifier.publicationtitleIEEE Journal on Selected Areas in Communicationsen
dc.identifier.publicationtitleIEEE journal on selected areas in communicationsen
dc.identifier.publicationvolume38
dc.identifier.urihttps://hdl.handle.net/10016/29419
dc.identifier.uxxiAR/0000024048
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/856950en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/ 761536en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/856709en
dc.rights© 2019 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherMobile networksen
dc.subject.otherNetwork slicingen
dc.subject.other5g networksen
dc.subject.otherArtificial intelligenceen
dc.subject.otherDeep learningen
dc.titleDeepCog: optimizing resource provisioning in network slicing with AI-based capacity forecastingen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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