Empowering beyond 5G Networks: An Experimental Assessment of Zero-Touch Management and Orchestration

Effective zero-touch management and orchestration (ZSM&O) is crucial for scaling network slicing, particularly transitioning toward Beyond 5G (B5G) and 6G networks. This paper empirically validates the network slicing framework developed under the European Union Horizon 2020 MonB5G project. Buil...

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Bibliographic Details
Authors: Barrachina-Munoz S., Rezazadeh F., Blanco L., Kuklinski S., Zeydan E., Chawla A., Zanzi L., Devoti F., Vlahodimitropoulou V., Chochliouros I., Bosneag A.-M., Cherrared S., Vettori L., Mangues-Bafalluy J.
Format: article
Status:Published version
Publication Date:2024
Country:España
Institution:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Repository:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
OAI Identifier:oai:cttc.fundanetsuite.com:p8557
Online Access:https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8557
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211566465&doi=10.1109%2fACCESS.2024.3510804&partnerID=40&md5=3f94b78b92bf31a626c4eb143ae6cee4
Access Level:Open access
Keyword:3G mobile communication systems
5G mobile communication systems
Deep reinforcement learning
Queueing networks
Reinforcement learning
Testbeds
Virtualization
Beyond 5g
European union
Experimental assessment
Horizon 2020
Industry collaboration
Modulars
Network slicing
Networks management
Orchestration
Scalings
Radio access networks
Description
Summary:Effective zero-touch management and orchestration (ZSM&O) is crucial for scaling network slicing, particularly transitioning toward Beyond 5G (B5G) and 6G networks. This paper empirically validates the network slicing framework developed under the European Union Horizon 2020 MonB5G project. Building on three years of academia-industry collaboration, MonB5G introduces a flexible slicing model featuring umbrella slices that orchestrate modular, specialized slices across multi-domain environments to address next-generation service demands. For the first time, we evaluate its practicality in a 5G cloud-native testbed through a virtual reality (VR) streaming use case, supported by solutions such as federated learning-based CPU forecasting, anomaly detection, and deep reinforcement learning (DRL) for radio access network (RAN) optimization. The paper offers insights from technically demanding experimental tests and highlights challenges and development paths for managing next-generation mobile networks. © 2013 IEEE.

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