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Smart city article details
| Title | Network Management-Edge And Cloud Computing The Slicenet Case |
|---|---|
| ID_Doc | 39001 |
| Authors | Weiss M.B.; Gavras A.; Salva-Garcia P.; Alcaraz-Calero J.M.; Wang Q. |
| Year | 2020 |
| Published | 2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020 |
| DOI | http://dx.doi.org/10.1109/CCNC46108.2020.9045678 |
| Abstract | The new Fifth-Generation (5G) mobile networks entail next-generation network management solutions to manage both physical and virtual network infrastructures and services. The challenge is to effectively manage the increased complexity due to virtualization and softwarization, whilst attempting to reduce the operational costs for 5G operators. This paper focuses on the approach of the EU Horizon 2020 5G-PPP project SliceNet to meet this challenge. SliceNet is implementing an intelligence-based autonomic end-to-end slicing-friendly infrastructure for 5G networks. The paper describes SliceNet's virtualized Mobile/Multi-access Edge Computing (MEC) infrastructure segment as a solution to manage the combination of edge and cloud computing for the new services emerging on the vertical industries as part of the new 5G mobile networks. It presents the vision and recent development of the project on the MEC part of the architecture, and the artificial intelligence approach being investigated in the project. Moreover, the paper introduces three representative use cases to describe how the framework organizes between cloud and edge. These use cases show how the MEC and cloud computing can be combined for services in e-health, smart-grids, and smart-cities verticals. © 2020 IEEE. |
| Author Keywords | 5G network; artificial intelligence; cloud; machine learning; mobile edge; network management |
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