Online Approach for Proactive Scaling of Mobile Core Network Functions - Ferreira, Abrahão; Tavares, Kauan; Vidal, Douglas; Lins, Silvia; Klautau, Aldebaro; Bonato, Cristiano; Gonçalves, Glauco (Anais do Workshop de Redes 6G (W6G), 2025)
New use cases for 6G networks introduce stringent requirements, like massive connectivity, which demand effective scaling of core functions. This is often achieved through scaling techniques that utilize machine learning models to proactively predict traffic demand. However, these models face challenges concerning concept drifts, i.e., changes in the statistical patterns previously learned by the model. To address this limitation, this study evaluates a method based on online learning for scheduling core functions. The method was tested using data from a mobile network, simulating scenarios with concept drift. The results demonstrate that the online model effectively adapts to changes, reducing prediction errors and maintaining stability in dynamic environments.