| Texto completo | |
| Autor(es): |
Moreira, Rodrigo
;
Pasquini, Rafael
;
Martins, Joberto S. B.
;
Carvalho, Tereza C.
;
Silva, Flavio de Oliveira
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE; v. 174, p. 15-pg., 2026-01-01. |
| Resumo | |
Network Slicing (NS) realization requires AI-native orchestration architectures to efficiently and intelligently handle heterogeneous user requirements. To achieve this, network slicing is evolving towards a more user-centric digital transformation, focusing on architectures that incorporate native intelligence to enable self-managed connectivity in an integrated and isolated manner. However, these initiatives face the challenge of validating their results in production environments, particularly those utilizing ML-enabled orchestration, as they are often tested in local networks or laboratory simulations. This paper proposes a large-scale validation method using a network slicing prediction model to forecast latency using Deep Neural Networks (DNNs) and basic ML algorithms embedded within an NS architecture evaluated in real large-scale production testbeds. It measures and compares the performance of different DNNs and ML algorithms, considering a distributed database application deployed as a network slice over two large-scale production testbeds. The investigation highlights how AI-based prediction models can enhance network slicing orchestration architectures and presents a seamless, production-ready validation method as an alternative to fully controlled simulations or laboratory setups. (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 |