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Artificial Intelligence for Elastic Management and Orchestration of 5G Networks

Gutierrez-Estevez, David; Gramaglia, Marco; De Domenico, Antonio; Dandachi, Ghina; Khatibi, Sina; Tsolkas, Dimitris; Balan, Irina; Garcia-Saavedra, Andres; Elzur, Uri; Wang, Yue

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  <identifier identifierType="DOI">10.5281/zenodo.3266981</identifier>
      <creatorName>Gutierrez-Estevez, David</creatorName>
      <affiliation>Samsung  Research  UK</affiliation>
      <creatorName>Gramaglia, Marco</creatorName>
      <affiliation>University Carlos III of Madrid</affiliation>
      <creatorName>De Domenico, Antonio</creatorName>
      <familyName>De Domenico</familyName>
      <affiliation>CEA Leti France</affiliation>
      <creatorName>Dandachi, Ghina</creatorName>
      <affiliation>CEA Leti France</affiliation>
      <creatorName>Khatibi, Sina</creatorName>
      <affiliation>Nomor Research Germany</affiliation>
      <creatorName>Tsolkas, Dimitris</creatorName>
      <affiliation>Mobics Greece</affiliation>
      <creatorName>Balan, Irina</creatorName>
      <affiliation>Nokia  Bell  Labs  German</affiliation>
      <creatorName>Garcia-Saavedra, Andres</creatorName>
      <affiliation>NEC Research Laboratories Europe GmbH, Germany</affiliation>
      <creatorName>Elzur, Uri</creatorName>
      <affiliation>Intel HQ</affiliation>
      <creatorName>Wang, Yue</creatorName>
      <affiliation>Samsung  Research  UK</affiliation>
    <title>Artificial Intelligence for Elastic Management and Orchestration of 5G Networks</title>
    <date dateType="Issued">2019-07-03</date>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3266980</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution Non Commercial No Derivatives 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;The emergence of 5G enables a broad set of diversified and heterogeneous services with complex and potentially conflicting demands. For networks to be able to satisfy those needs, a flexible, adaptable, and programmable architecture based on network slicing is being proposed. Moreover, a softwarization and cloudification of the communications networks is required, where network functions (NFs) are being transformed from programs running on dedicated hardware platforms to programs running over a shared pool of computational and communication resources. This architectural framework allows the introduction of resource elasticity as a key means to make an efficient use of the computational resources of 5G systems, but adds challenges related to resource sharing and efficiency. In this paper, we propose Artificial Intelligence (AI) as a built-in architectural feature that allows the exploitation of the resource elasticity of a 5G network. Building on the work of the recently formed Experiential Network Intelligence (ENI) industry specification group of the European Telecommunications Standards Institute (ETSI) to embed an AI engine in the network, we describe a novel taxonomy for learning mechanisms that target exploiting the elasticity of the network as well as three different resource elastic use cases leveraging AI. This work describes the basis of a use case recently approved at ETSI ENI.&lt;/p&gt;</description>
    <description descriptionType="Other">© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.</description>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/761445/">761445</awardNumber>
      <awardTitle>5G Mobile Network Architecture for diverse services, use cases, and applications in 5G and beyond</awardTitle>
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