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.

5Growth Data-Driven AI-Based Scaling / De Vleeschauwer, Danny; Baranda, Jorge; Mangues-Bafalluy, Josep; Chiasserini, Carla Fabiana; Malinverno, Marco; Puligheddu, Corrado; Magoula, Lina; Martin-Perez, Jorge; Barmpounakis, Sokratis; Kondepu, Koteswararao; Valcarenghi, Luca; Li, Xi; Papagianni, Chrysa; Garcia-Saavedra, Andres. - STAMPA. - (2021). ((Intervento presentato al convegno 2021 EuCNC & 6G Summit tenutosi a Virtual conference due to COVID-19 nel 8-11 June 2021 [10.1109/EuCNC/6GSummit51104.2021.9482476].

5Growth Data-Driven AI-Based Scaling

Carla Fabiana Chiasserini;Marco Malinverno;Corrado Puligheddu;
2021

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2891315