5734869
doi
10.5281/zenodo.5734869
oai:zenodo.org:5734869
user-h2020daemon
user-eu
Andres Garcia-Saavedra
NEC Laboratories Europe GmbH
Xavier Costa-Perez
NEC Laboratories Europe GmbH, i2cat, ICREA
George Iosifidis
Delft University of Technology
EdgeBOL: Automating Energy-savings for Mobile Edge AI
Jose A. Ayala-Romero
Huawei Ireland Research Center
doi:10.1145/3485983.3494849
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Mobile networks
O-RAN
energy efficiency
QoS
<p>Supporting Edge AI services is one of the most exciting features of future mobile networks. These services involve the collection and processing of voluminous data streams, right at the network edge, so as to offer real-time and accurate inferences to users. However, their widespread deployment is hampered by the energy cost they induce to the network. To overcome this obstacle, we propose a Bayesian learning framework for jointly configuring the service and the Radio Access Network (RAN), aiming to minimize the total energy consumption while respecting desirable accuracy and latency thresholds. Using a fully-fledged prototype with a software-defined base station (BS) and a GPU-enabled edge server, we profile a state-of-the-art video analytics AI service and identify new performance trade-offs. Accordingly, we tailor the optimization framework to account for the network context, the user needs, and the service metrics. The efficacy of our proposal is verified in a series of experiments and comparisons with neural network-based benchmarks.</p>
Zenodo
2021-11-29
info:eu-repo/semantics/preprint
5734868
user-h2020daemon
user-eu
award_title=Network intelligence for aDAptive and sElf-Learning MObile Networks; award_number=101017109; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/101017109; funder_id=00k4n6c32; funder_name=European Commission;
1638193724.112587
4816
md5:4878e06a8bef6a37fe65220148feed2d
https://zenodo.org/records/5734869/files/conext21-final106.pdf
public
10.1145/3485983.3494849
Is published in
doi
10.5281/zenodo.5734868
isVersionOf
doi