November 29, 2021 Preprint Open Access

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

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  <identifier identifierType="DOI">10.5281/zenodo.5734886</identifier>
  <creators>
    <creator>
      <creatorName>Jose A. Ayala-Romero</creatorName>
      <affiliation>Huawei Ireland Research Center</affiliation>
    </creator>
    <creator>
      <creatorName>Andres Garcia-Saavedra</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2005-2222</nameIdentifier>
      <affiliation>NEC Laboratories Europe GmbH</affiliation>
    </creator>
    <creator>
      <creatorName>Xavier Costa-Perez</creatorName>
      <affiliation>NEC Laboratories Europe GmbH, i2cat, ICREA</affiliation>
    </creator>
    <creator>
      <creatorName>George Iosifidis</creatorName>
      <affiliation>Delft University of Technology</affiliation>
    </creator>
  </creators>
  <titles>
    <title>EdgeBOL: Automating Energy-savings for Mobile Edge AI</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Mobile networks</subject>
    <subject>O-RAN</subject>
    <subject>energy efficiency</subject>
    <subject>QoS</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-11-29</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Preprint"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5734886</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsPublishedIn">10.1145/3485983.3494849</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5734868</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020daemon</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/101017109/">101017109</awardNumber>
      <awardTitle>Network intelligence for aDAptive and sElf-Learning MObile Networks</awardTitle>
    </fundingReference>
  </fundingReferences>
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