dc.contributor.author | Iosifidis, Georgios | |
dc.date.accessioned | 2022-05-06T15:19:37Z | |
dc.date.available | 2022-05-06T15:19:37Z | |
dc.date.issued | 2021 | |
dc.date.submitted | 2021 | en |
dc.identifier.citation | J. A. Ayala-Romero, A. Garcia-Saavedra, X. Costa-Perez and G. Iosifidis, "Demonstrating a Bayesian Online Learning for Energy-Aware Resource Orchestration in vRANs," IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2021, pp. 1-2 | en |
dc.identifier.other | Y | |
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 demonstrate a novel machine learning approach to solve resource orchestration problems in energy-constrained vRANs. Specifically, we demonstrate 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 - converge an order of magnitude faster than other machine learning methods - and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the ad-vantages of our approach in a testbed comprised of fully-fledged LTE stacks and a power meter, and implementing our approach into O-RAN's non-real-time RAN Intelligent Controller (RIC). | en |
dc.language.iso | en | en |
dc.relation.ispartofseries | IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021; | |
dc.relation.ispartofseries | 9484585; | |
dc.rights | Y | en |
dc.subject | RAN Intelligent Controller (RIC). | en |
dc.subject | Radio Access Network Virtualization (vRAN) | en |
dc.subject | cells-on-wheels (e.g., drones) | en |
dc.title | Demonstrating a bayesian online learning for energy-aware resource orchestration in vRANs | en |
dc.type | Conference Paper | en |
dc.type.supercollection | scholarly_publications | en |
dc.type.supercollection | refereed_publications | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/iosifidg | |
dc.identifier.rssinternalid | 242732 | |
dc.identifier.doi | http://dx.doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484585 | |
dc.rights.ecaccessrights | openAccess | |
dc.identifier.orcid_id | 0000-0003-1001-2323 | |
dc.contributor.sponsor | Science Foundation Ireland | en |
dc.contributor.sponsorGrantNumber | SFI 17/CDA/4760 | en |
dc.identifier.uri | http://hdl.handle.net/2262/98556 | |