5767768
doi
10.5281/zenodo.5767768
oai:zenodo.org:5767768
user-h2020daemon
user-eu
Miguel Camelo
University of Antwerp - imec, IDLab
Luca Cominardi
ADLINK Technology
Steven Latré
University of Antwerp - imec, IDLab
Johann M. Marquez-Barja
University of Antwerp - imec, IDLab
Building Realistic Experimentation Environments for AI-enhanced Management and Orchestration (MANO) of 5G and beyond V2X systems
Nina Slamnik-Kriještorac
University of Antwerp - imec, IDLab
doi:10.1109/CCNC49033.2022.9700649
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
mangement and orchestration
NFV
AI
ML
zenoh
testbeds
experimentation
<p>The plethora of heterogeneous and diversified services in 5G and beyond requires from networks to be flexible, adaptable, and programmable, i.e., to be able to correspondingly adapt to changes. As human intervention might significantly increase delays in MANagement and Orchestration (MANO) operations, automation and intelligence become imperative for orchestrating services and resources, especially the ones with stringent requirements for latency and capacity, such as Vehicle-to-Everything (V2X) services. As virtualization and Artificial Intelligence (AI) promise to mitigate those challenges towards enabling true automation in MANO operations, in this paper we present our effort towards building and fully utilizing the reallife testbeds, such as Smart Highway and Virtual Wall, located in Belgium, to conduct realistic experimentation and validation of distributed orchestration intelligence in a dynamic network such as V2X system.</p>
This work has been performed in the framework of the European Union's Horizon 2020 project DAEMON co-funded by the EU under grant agreement No. 101017109, the Horizon 2020 Fed4FIRE+ project, Grant Agreement No. 723638, and the Horizon 2020 5G-Blueprint project under Agreement No. 952189.
Zenodo
2021-12-08
info:eu-repo/semantics/workingPaper
5767767
user-h2020daemon
user-eu
award_title=Network intelligence for aDAptive and sElf-Learning MObile Networks; award_number=101017109; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/101017109; funder_id=00k4n6c32; funder_name=European Commission;
1689169908.260829
1695261
md5:6a620b1acbbd91110aaa631d91c0bea7
https://zenodo.org/records/5767768/files/CCNC_WiP_2022.pdf
public
10.1109/CCNC49033.2022.9700649
Is published in
doi
10.5281/zenodo.5767767
isVersionOf
doi