Demonstrating a bayesian online learning for energy-aware resource orchestration in vRANs
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2021Author:
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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. 1-2Download Item:
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).
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Grant Number
Science Foundation Ireland
SFI 17/CDA/4760
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http://people.tcd.ie/iosifidg
Author: Iosifidis, Georgios
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Science Foundation IrelandType of material:
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IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021;9484585;
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RAN Intelligent Controller (RIC)., Radio Access Network Virtualization (vRAN), cells-on-wheels (e.g., drones)DOI:
http://dx.doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484585The following license files are associated with this item: