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Integration of IoT, Transport SDN and Edge/Cloud computing for Dynamic Distribution of IoT Analytics and Efficient Use of Network Resources

Muñoz, Raul; Vilalta, Ricard; Yoshikane, Noboru; Casellas, Ramon; Martínez, Ricardo; Tsuritani, Takehiro; Morita, Itsuro

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<dc:creator>Muñoz, Raul</dc:creator>
<dc:creator>Vilalta, Ricard</dc:creator>
<dc:creator>Yoshikane, Noboru</dc:creator>
<dc:creator>Casellas, Ramon</dc:creator>
<dc:creator>Martínez, Ricardo</dc:creator>
<dc:creator>Tsuritani, Takehiro</dc:creator>
<dc:creator>Morita, Itsuro</dc:creator>
<dc:date>2018-04-01</dc:date>
<dc:description>Internet of Things (IoT) requires cloud infrastructures for data analysis (e.g., temperature monitoring, energy consumption measurement, etc.). Traditionally, cloud services have been implemented in large datacenters in the core network. Core cloud offers high-computational capacity with moderate response time, meeting the requirements of centralized services with low-delay demands. However, collecting information and bringing it into one core cloud infrastructure is not a long-term scalable solution, particularly as the volume of IoT devices and data is forecasted to explode. A scalable and efficient solution, both at the network and cloud level, is to distribute the IoT analytics between the core cloud and the edge of the network (e.g., first analytics on the edge cloud and the big data analytics on the core cloud). For an efficient distribution of IoT analytics and use of network resources, it requires to integrate the control of the transport networks (packet and optical) with the distributed edge and cloud resources in order to deploy dynamic and efficient IoT services. This paper presents and experimentally validates the first IoT-aware multilayer (packet/optical) transport software defined networking and edge/cloud orchestration architecture that deploys an IoT-traffic control and congestion avoidance mechanism for dynamic distribution of IoT processing to the edge of the network (i.e., edge computing) based on the actual network resource state.</dc:description>
<dc:description>Grant numbers : This research also has been partly funded by Spanish MINECO projects DESTELLO (TEC2015-69256-R). © 2018 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.</dc:description>
<dc:identifier>https://zenodo.org/record/1421728</dc:identifier>
<dc:identifier>10.1109/JLT.2018.2800660</dc:identifier>
<dc:identifier>oai:zenodo.org:1421728</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>info:eu-repo/grantAgreement/EC/H2020/761510/</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:source>IEEE Journal of lightwave Technologies 36(7) 1420 - 1428</dc:source>
<dc:subject>IoT analytics</dc:subject>
<dc:subject>edge computing</dc:subject>
<dc:subject>transport SDN</dc:subject>
<dc:subject>cloud orchestration</dc:subject>
<dc:title>Integration of IoT, Transport SDN and Edge/Cloud computing for Dynamic Distribution of IoT Analytics and Efficient Use of Network Resources</dc:title>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>publication-article</dc:type>
</oai_dc:dc>

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