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

Conference Paper (2021)
Author(s)

Jose A. Ayala-Romero (Trinity College Dublin)

Andres Garcia-Saavedra (NEC Laboratories Europe)

Xavier Costa-Perez (NEC Laboratories Europe, I2CAT Foundation)

George Iosifidis (TU Delft - Embedded Systems)

Research Group
Embedded Systems
Copyright
© 2021 Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez, G. Iosifidis
DOI related publication
https://doi.org/10.1109/INFOCOM42981.2021.9488845
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez, G. Iosifidis
Research Group
Embedded Systems
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. @en
ISBN (electronic)
9780738112817
Reuse Rights

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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.

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