EdgeBOL: Automating energy-savings for mobile edge AI

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

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

8 Citations (Scopus)
32 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationCoNEXT 2021 - Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies
PublisherAssociation for Computing Machinery (ACM)
Pages397-410
Number of pages14
ISBN (Electronic)978-1-4503-9098-9
DOIs
Publication statusPublished - 2021
Event17th ACM International Conference on emerging Networking EXperiments and Technologies, CoNEXT 2021 - Virtual, Online, Germany
Duration: 7 Dec 202110 Dec 2021

Publication series

NameCoNEXT 2021 - Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies

Conference

Conference17th ACM International Conference on emerging Networking EXperiments and Technologies, CoNEXT 2021
Country/TerritoryGermany
CityVirtual, Online
Period7/12/2110/12/21

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

  • Energy efficiency
  • Mobile networks
  • O-RAN
  • QoS

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