Bayesian online learning for energy-aware resource orchestration in virtualized RANs

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dc.contributor.author Ayala Romero, José A.
dc.contributor.author Garcia-Saavedra, Andres
dc.contributor.author Costa-Perez, Xavier
dc.contributor.author Iosifidis, George
dc.date.accessioned 2022-02-14T11:08:28Z
dc.date.available 2022-02-14T11:08:28Z
dc.date.issued 2021-05-10
dc.identifier.bibliographicCitation Ayala-Romero, J. A., Garcia-Saavedra, A., Costa-Perez, X. & Iosifidis, G. (10-13 May 2021). Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs [proceedings]. IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, Vancouver, BC, Canada.
dc.identifier.isbn 978-1-6654-0325-2 (Electronic)
dc.identifier.isbn 978-1-6654-3131-6 (Print on Demand(PoD))
dc.identifier.issn 2641-9874 (Electronic)
dc.identifier.issn 0743-166X (Print on Demand(PoD))
dc.identifier.uri http://hdl.handle.net/10016/34113
dc.description Proceedings of: IEEE International Conference on Computer Communications, 10-13 May 2021, Vancouver, BC, Canada.
dc.description.abstract Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We perform an in-depth experimental analysis of the energy consumption of virtualized Base Stations (vBSs) and render two conclusions: (i) characterizing performance and power consumption is intricate as it depends on human behavior such as network load or user mobility; and (ii) there are many control policies and some of them have non-linear and monotonic relations with power and throughput. Driven by our experimental insights, we argue that machine learning holds the key for vBS control. We formulate two problems and two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the convergence and flexibility of our approach and assess its performance using an experimental prototype.
dc.description.sponsorship This work was supported by the European Commission through Grant No. 856709 (5Growth) and Grant No. 101017109 (DAEMON); and by SFI through Grant No. SFI 17/CDA/4760.
dc.format.extent 10
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2021 IEEE.
dc.subject.other Energy consumption
dc.subject.other Base stations
dc.subject.other Power demand
dc.subject.other Machine learning
dc.subject.other Telecommunication traffic
dc.subject.other Throughput
dc.subject.other Bayes methods
dc.title Bayesian online learning for energy-aware resource orchestration in virtualized RANs
dc.type conferenceObject
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/INFOCOM42981.2021.9488845
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/856709
dc.relation.projectID info:eu-repo/grantAgreement/EC/101017109
dc.type.version acceptedVersion
dc.relation.eventdate 2021-05-10
dc.relation.eventplace Vancouver
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 1
dc.identifier.publicationlastpage 10
dc.identifier.publicationtitle IEEE INFOCOM 2021 - IEEE Conference on Computer Communications
dc.contributor.funder European Commission
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