10.5281/zenodo.5734886
https://zenodo.org/records/5734886
oai:zenodo.org:5734886
Jose A. Ayala-Romero
Jose A. Ayala-Romero
Huawei Ireland Research Center
Andres Garcia-Saavedra
Andres Garcia-Saavedra
0000-0003-2005-2222
NEC Laboratories Europe GmbH
Xavier Costa-Perez
Xavier Costa-Perez
NEC Laboratories Europe GmbH, i2cat, ICREA
George Iosifidis
George Iosifidis
Delft University of Technology
EdgeBOL: Automating Energy-savings for Mobile Edge AI
Zenodo
2021
Mobile networks
O-RAN
energy efficiency
QoS
2021-11-29
eng
10.1145/3485983.3494849
10.5281/zenodo.5734868
https://zenodo.org/communities/h2020daemon
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
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.
European Commission
10.13039/501100000780
101017109
Network intelligence for aDAptive and sElf-Learning MObile Networks