Network management - edge and cloud computing the SliceNet case

Maria Barros Weiss, Anastasius Gavras, Pablo Salva-Garcia, Jose M. Alcaraz-Calero, Qi Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)
Place of PublicationPiscataway, NJ
Number of pages6
ISBN (Electronic)9781728138930
ISBN (Print)9781728138947
Publication statusPublished - 26 Mar 2020


  • 5G network
  • Network management
  • Cloud
  • Mobile edge
  • Artificial intelligence
  • Machine learning


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