RT Conference Proceedings
T1 5Growth data-driven AI-based scaling
A1 De Vleeschauwer, Danny
A1 Baranda, Jorge
A1 Mangues-Bafalluy, Josep
A1 Chiasserini, Carla Fabiana
A1 Malinverno, Marco
A1 Puligheddu, Corrado
A1 Magoula, Lina
A1 Martín Pérez, Jorge
A1 Barmpounakis, Sokratis
A1 Kondepu, Koteswararao
A1 Valcarenzhi, Luca
A1 Li, Xi
A1 Papagianni, Chrysa
A1 García Saavedra, Andrés
AB This paper presents a data-driven approach leveraging AI/ML models to automate the service scaling operation and, in this way, meet the service requirements while minimizing the consumption of network, computing, and storage resources. This approach is integrated into the 5Growth service management software platform. In particular, a prototype was developed to demonstrate how the novel 5Growth AI/ML platform can be used in a closed-loop automation system to support the automated service scaling operation. Furthermore, a number of additional ML-based approaches are developed in the context of eMBB and C-V2N scenarios, which can be embedded into the system for handling more complex use cases.
PB IEEE
SN 978-1-6654-1526-2 (Electronic)
SN 978-1-6654-3021-0 (Print on Demand(PoD))
YR 2021
FD 2021-06-08
LK https://hdl.handle.net/10016/34124
UL https://hdl.handle.net/10016/34124
LA eng
NO Proceedings of: Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 8-11 June 2021, Porto, Portugal.
NO This work has been partially supported by EC H2020 5GPPP 5Growth project (Grant 856709).
DS e-Archivo
RD 27 jul. 2024