1 Ocak 2015 Dergi makalesi Açık Erişim

Bastug, Ejder; Bennis, Mehdi; Zeydan, Engin; Kader, Manhal Abdel; Karatepe, Ilyas Alper; Er, Ahmet Salih; Debbah, Merouane

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/81633</identifier>
  <creators>
    <creator>
      <creatorName>Bastug, Ejder</creatorName>
      <givenName>Ejder</givenName>
      <familyName>Bastug</familyName>
      <affiliation>Univ Paris Saclay, Ctr Supelec, Large Networks &amp; Syst Grp LANEAS, Gif Sur Yvette, France</affiliation>
    </creator>
    <creator>
      <creatorName>Bennis, Mehdi</creatorName>
      <givenName>Mehdi</givenName>
      <familyName>Bennis</familyName>
      <affiliation>Univ Oulu, Ctr Wireless Commun, SF-90100 Oulu, Finland</affiliation>
    </creator>
    <creator>
      <creatorName>Zeydan, Engin</creatorName>
      <givenName>Engin</givenName>
      <familyName>Zeydan</familyName>
      <affiliation>AveaLabs, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Kader, Manhal Abdel</creatorName>
      <givenName>Manhal Abdel</givenName>
      <familyName>Kader</familyName>
      <affiliation>Univ Paris Saclay, Ctr Supelec, Large Networks &amp; Syst Grp LANEAS, Gif Sur Yvette, France</affiliation>
    </creator>
    <creator>
      <creatorName>Karatepe, Ilyas Alper</creatorName>
      <givenName>Ilyas Alper</givenName>
      <familyName>Karatepe</familyName>
      <affiliation>AveaLabs, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Er, Ahmet Salih</creatorName>
      <givenName>Ahmet Salih</givenName>
      <familyName>Er</familyName>
      <affiliation>AveaLabs, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Debbah, Merouane</creatorName>
      <givenName>Merouane</givenName>
      <familyName>Debbah</familyName>
    </creator>
  </creators>
  <titles>
    <title>Big Data Meets Telcos: A Proactive Caching Perspective</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2015</publicationYear>
  <dates>
    <date dateType="Issued">2015-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/81633</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/JCN.2015.000102</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: Velocity, voracity, volume, and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul of-floadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platform and the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4 Gbyte of storage size (87% of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.</description>
  </descriptions>
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