Demonstrating a Bayesian Online Learning for Energy-Aware Resource Orchestration in vRANs

Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez, George Iosifidis

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
1 Downloads (Pure)

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).
Original languageEnglish
Title of host publicationIEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Place of PublicationPiscataway
PublisherIEEE
Pages1-2
Number of pages2
ISBN (Electronic)978-1-6654-0443-3
ISBN (Print)978-1-6654-4714-0
DOIs
Publication statusPublished - 2021
EventIEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) - Virtual/Vancouver, Canada
Duration: 10 May 202113 May 2021

Workshop

WorkshopIEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
CountryCanada
CityVirtual/Vancouver
Period10/05/2113/05/21

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Meters
  • Power demand
  • Machine learning algorithms
  • Conferences
  • Machine learning
  • Bayes methods
  • Virtualization

Fingerprint

Dive into the research topics of 'Demonstrating a Bayesian Online Learning for Energy-Aware Resource Orchestration in vRANs'. Together they form a unique fingerprint.

Cite this