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Orchestrating energy-efficient vRANs: Bayesian learning and experimental results

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dc.contributor.authorAyala Romero, José A.
dc.contributor.authorGarcía-Saavedra, Andrés
dc.contributor.authorCosta-Pérez, Xavier
dc.contributor.authorIosifidis, George
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2023-09-21T14:51:43Z
dc.date.available2023-09-21T14:51:43Z
dc.date.issued2023-05-01
dc.description.abstractVirtualized base stations (vBS) can be implemented in diverse commodity platforms and are expected to bring unprecedented operational flexibility and cost efficiency to the next generation of cellular networks. However, their widespread adoption is hampered by their complex configuration options that affect in a non-traditional fashion both their performance and their power consumption requirements. Following an in-depth experimental analysis in a bespoke testbed, we characterize the vBS power cost profile and reveal previously unknown couplings between their various control knobs. Motivated by these findings, we develop a Bayesian learning framework for the orchestration of vBSs and design two novel algorithms: (i) BP-vRAN, which employs online learning to balance the vBS performance and energy consumption, and (ii) SBP-vRAN, which augments our optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, i.e., converge an order of magnitude faster than state-of-the-art Deep Reinforcement Learning methods, and achieve optimal performance. We demonstrate the efficacy of these solutions in an experimental prototype using real traffic traces.en
dc.description.sponsorshipThis work has been supported by the European Commission through Grant No. 101017109 (DAEMON project), and the CERCA Programme/Generalitat de Catalunya.en
dc.format.extent16
dc.identifier.bibliographicCitationAyala-Romero, J. A.; García-Saavedra, A.; Costa-Pérez, X.; Iosifidis, G. Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results. In: IEEE Transactions on Mobile Computing, 22(5), May 2023, Pp. 2910-2924en
dc.identifier.doihttps://doi.org/10.1109/TMC.2021.3123794
dc.identifier.issn1536-1233
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue00
dc.identifier.publicationlastpage16
dc.identifier.publicationtitleIEEE Transactions on Mobile Computingen
dc.identifier.publicationvolume00
dc.identifier.urihttps://hdl.handle.net/10016/34177
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/101017109
dc.rights© The authors 2021. This work is licensed under a Creative Commons Attribution 4.0 License.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherBayesian learningen
dc.subject.otherGaussian processesen
dc.subject.otherOnline learningen
dc.subject.otherRadio access networksen
dc.subject.otherEnergy efficiencyen
dc.subject.otherGreen networksen
dc.subject.otherNetwork virtualizationen
dc.subject.otherWireless testbedsen
dc.titleOrchestrating energy-efficient vRANs: Bayesian learning and experimental resultsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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