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

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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 Pérez, Xavier
dc.date.accessioned 2019-05-07T09:15:04Z
dc.date.available 2019-05-07T09:15:04Z
dc.date.issued 2019-09-23
dc.identifier.bibliographicCitation IEEE INFOCOM 2019- IEEE Conference on Computer Communications, 29 April-2 May 2019, Paris, France [proceedings], 6 pp.
dc.identifier.uri http://hdl.handle.net/10016/28332
dc.description Proceeding 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, France
dc.description.abstract Orchestrating 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.
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 Europe Ltd. by the 5GTransformer project (Grant Agreement No. 761536).
dc.format.extent 6
dc.language.iso eng
dc.rights © 2019 IEEE.
dc.subject.other 5G networks
dc.subject.other Deep learning
dc.subject.other Mobile networks
dc.title 𝛼-OMC: cost-aware deep learning for mobile network resource orchestration
dc.title.alternative Alfa-OMC: cost-aware deep learning for mobile network resource orchestration
dc.type conferenceObject
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/INFCOMW.2019.8845178
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grant/Agreeement/EC/H2020/761445/5G-MoNArch
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/761536/5G Transformer
dc.type.version submittedVersion
dc.relation.eventdate 2019, April 29
dc.relation.eventnumber 12
dc.relation.eventplace Paris (France)
dc.relation.eventtitle International Workshop on Network Intelligence (NI 2019): Machine Learning for Networking (part of 2019 IEEE International Conference on Computer Communications (IEEE INFOCOM 2019))
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 1
dc.identifier.publicationlastpage 6
dc.identifier.uxxi CC/0000029120
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