Publication: Orchestrating energy-efficient vRANs: Bayesian learning and experimental results
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.contributor.funder | European Commission | en |
dc.date.accessioned | 2023-09-21T14:51:43Z | |
dc.date.available | 2023-09-21T14:51:43Z | |
dc.date.issued | 2023-05-01 | |
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. | en |
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. | en |
dc.format.extent | 16 | |
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 | en |
dc.identifier.doi | https://doi.org/10.1109/TMC.2021.3123794 | |
dc.identifier.issn | 1536-1233 | |
dc.identifier.publicationfirstpage | 1 | |
dc.identifier.publicationissue | 00 | |
dc.identifier.publicationlastpage | 16 | |
dc.identifier.publicationtitle | IEEE Transactions on Mobile Computing | en |
dc.identifier.publicationvolume | 00 | |
dc.identifier.uri | https://hdl.handle.net/10016/34177 | |
dc.language.iso | eng | en |
dc.publisher | IEEE | en |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/101017109 | |
dc.rights | © The authors 2021. This work is licensed under a Creative Commons Attribution 4.0 License. | en |
dc.rights | Atribución 3.0 España | * |
dc.rights.accessRights | open access | en |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject.eciencia | Telecomunicaciones | es |
dc.subject.other | Bayesian learning | en |
dc.subject.other | Gaussian processes | en |
dc.subject.other | Online learning | en |
dc.subject.other | Radio access networks | en |
dc.subject.other | Energy efficiency | en |
dc.subject.other | Green networks | en |
dc.subject.other | Network virtualization | en |
dc.subject.other | Wireless testbeds | en |
dc.title | Orchestrating energy-efficient vRANs: Bayesian learning and experimental results | en |
dc.type | research article | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
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