| Texto completo | |
| Autor(es): |
da Silva, Bruno Marques
;
Rodrigues Moreira, Larissa F.
;
Moreira, Rodrigo
;
Silva, Flavio de Oliveira
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2025 JOINT EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS & 6G SUMMIT, EUCNC/6G SUMMIT; v. N/A, p. 6-pg., 2025-01-01. |
| Resumo | |
Integrating intelligence in mobile networks enhances resource management and ensures Service Level Agreements (SLAs) compliance. However, the complexity of subsystems and interactions in the network-slicing lifecycle poses challenges. Existing studies often rely on simulated datasets or monitoring systems, which face limitations in generalization, privacy, and real-time responsiveness due to data quality and complexity of the Machine Learning (ML) algorithm. Although advanced monitoring systems improve accuracy, their high computational cost necessitates low-overhead alternatives. This paper demonstrates that generic computing and network metrics in container-based 5G core can accurately reveal user experience in mobile networks, achieving a 10.5232% Mean Absolute Percentage Error (MAPE) with feature-based and sequence-based ML algorithms. (AU) | |
| Processo FAPESP: | 18/23097-3 - SFI2: fatiamento de infraestruturas de internet do futuro |
| Beneficiário: | Tereza Cristina Melo de Brito Carvalho |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |