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DAEMON Deliverable 3.1: Initial design of real-time control and VNF intelligence mechanisms

Authors :
Gramaglia, Marco
Naram Mhaisen
Garcia-Saavedra, Andres
Garcia, Gines
Chang, Chia-Yu
De Vleeschauwer, Danny
Molina, Maria
Slamnik-Krijestorac, Nina
Soto, Paola
Camelo, Miguel
Fuentes, Lidia
Amor, Mercedes
Pinto, Monica
Ca��ete, ��ngel
Munoz, Daniel-Jesus
Fiore, Marco
Gucciardo, Michele
Lutu, Andra
Publication Year :
2021
Publisher :
Zenodo, 2021.

Abstract

This deliverable presents DAEMON���s initial view on the problem of real-time control and network functions intelligence. The work presented in this document tackles two main points: ��� Understanding what the main challenges from the architectural perspective are and proposing initial solutions to overcome them. This activity has a strong relationship with the work carried out in WP2 in terms of requirements and architectural design. Namely, we identified additional requirements that are specifically related to the fast timescale NI applications. ��� The design of specific NI algorithms that focus on the inclusion of NI very close to the user plane. A lot of attention was paid to edge network functions, such as those related to the access network (including Reconfigurable Intelligent Surfaces). In total, we describe 11 algorithms. These algorithms can be generally categorized into two main classes: ��� Algorithms that jointly optimize radio and compute resources for intelligent NFV in vRANs. These algorithms are mainly presented in section 4 and deploy novel mechanisms and techniques for solving such difficult and coupled decision-making problems. ��� Algorithms that are capable of running on different timescales. These algorithms are of specific interest to the work package as they directly tackle the highly heterogeneous nature of edge and fog domains and are mainly presented in section 5. Each proposed algorithm specifically mentions the targeted KPI so as to create a clear, propaedeutic environment for the follow-up WP5 activities.<br />This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 101017109.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....8e291f6f1614440f98d8dbc322b54f44
Full Text :
https://doi.org/10.5281/zenodo.5745433