Orchestrating energy-efficient vRANs: Bayesian learning and experimental results

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dc.contributor.author Ayala Romero, José A.
dc.contributor.author García-Saavedra, Andrés
dc.contributor.author Costa-Pérez, Xavier
dc.contributor.author Iosifidis, George
dc.date.accessioned 2023-09-21T14:51:43Z
dc.date.available 2023-09-21T14:51:43Z
dc.date.issued 2023-05-01
dc.identifier.bibliographicCitation Ayala-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. 2910-2924
dc.identifier.issn 1536-1233
dc.identifier.uri http://hdl.handle.net/10016/34177
dc.description.abstract Virtualized 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.
dc.description.sponsorship This work has been supported by the European Commission through Grant No. 101017109 (DAEMON project), and the CERCA Programme/Generalitat de Catalunya.
dc.format.extent 16
dc.language.iso eng
dc.publisher IEEE
dc.rights © The authors 2021. This work is licensed under a Creative Commons Attribution 4.0 License.
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Bayesian learning
dc.subject.other Gaussian processes
dc.subject.other Online learning
dc.subject.other Radio access networks
dc.subject.other Energy efficiency
dc.subject.other Green networks
dc.subject.other Network virtualization
dc.subject.other Wireless testbeds
dc.title Orchestrating energy-efficient vRANs: Bayesian learning and experimental results
dc.type research article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/TMC.2021.3123794
dc.rights.accessRights open access
dc.relation.projectID info:eu-repo/grantAgreement/EC/101017109
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 00
dc.identifier.publicationlastpage 16
dc.identifier.publicationtitle IEEE Transactions on Mobile Computing
dc.identifier.publicationvolume 00
dc.contributor.funder European Commission
dc.type.hasVersion AM
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