Intelligent Service Orchestration in Edge Cloud Networks
The surge in data traffic is challenging for network infrastructure owners coping with stringent service requirements (e.g., high bandwidth, ultralow latency) as well as shrinking per-gigabyte revenues. Network softwarization and edge computing are powerful candidates to mitigate these issues. In pa...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) |
| Repositorio: | r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) |
| OAI Identifier: | oai:cttc.fundanetsuite.com:p6390 |
| Acceso en línea: | https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=6390 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119949680&doi=10.1109%2fMNET.101.2100214&partnerID=40&md5=2c92eab9bad396ddf79a95f0f9771555 |
| Access Level: | acceso abierto |
| Palabra clave: | Mobile telecommunication systems Wireless networks Cloud networks Data traffic Edge clouds Edge computing High bandwidth Intelligent Services Network infrastructure Service orchestration Service requirements Stringents Network architecture |
| Sumario: | The surge in data traffic is challenging for network infrastructure owners coping with stringent service requirements (e.g., high bandwidth, ultralow latency) as well as shrinking per-gigabyte revenues. Network softwarization and edge computing are powerful candidates to mitigate these issues. In parallel, there is an increasing demand for network virtualization and container-based services. In this study, we investigate the management of software defined networking (SDN)-based transport network and edge cloud service orchestration. To this end, we use a machine learning (ML)-based design to manage both transport and edge cloud resources of a mobile network effectively. To generate and use real-world data inside our ML platform, we use the Graphical Network Simulator-3 (GNS3) emulator environment. Our emulation results indicate that almost all of the trained ML models can accurately select the correct edge clouds (ECs) (i.e., with high test accuracy) under the considered two scenarios when transport and EC network parameters are considered in comparison to models trained via only transport or cloud-based parameters. At the end of the article, we also provide an evolved architecture where the proposed ML platform can be embedded in an end-to-end mobile network architecture and H2020 5Growth project's baseline management platform. © 2021 IEEE. |
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