Produção Nacional
Revisado por pares
Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration
2023; Institute of Electrical and Electronics Engineers; Volume: 11; Linguagem: Inglês
10.1109/access.2023.3292788
ISSN2169-3536
AutoresJoberto S. B. Martins, Tereza Cristina Melo de Brito Carvalho, Rodrigo Moreira, Cristiano Bonato Both, Adnei Donatti, João Henrique Corrêa, José Augusto Suruagy Monteiro, Sand Luz Corrêa, Antônio Abelém, Moisés R. N. Ribeiro, José Marcos S. Nogueira, Luiz Magalhães, Juliano Araújo Wickboldt, Tiago Ferreto, Ricardo C. Mello, Rafael Pasquini, Marcos Schwarz, Leobino N. Sampaio, Daniel F. Macedo, José Ferreira de Rezende, Kléber V. Cardoso, Flávio de Oliveira Silva,
Tópico(s)IoT and Edge/Fog Computing
ResumoNetwork Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications since it allows the optimization and customization of scarce and disputed resources among dynamic, demanding clients with highly distinct application requirements. Various standardization organizations, like 3GPP's proposal for new generation networks and state-of-the-art 5G/6G research projects, are proposing new NS architectures. However, new NS architectures have to deal with an extensive range of requirements that inherently result in having NS architecture proposals typically fulfilling the needs of specific sets of domains with commonalities. The Slicing Future Internet Infrastructures (SFI2) architecture proposal explores the gap resulting from the diversity of NS architectures target domains by proposing a new NS reference architecture with a defined focus on integrating experimental networks and enhancing the NS architecture with Machine Learning (ML) native optimizations, energy-efficient slicing, and slicing-tailored security functionalities. The SFI2 architectural main contribution includes the utilization of the slice-as-a-service paradigm for end-to-end orchestration of resources across multi-domains and multi-technology experimental networks. In addition, the SFI2 reference architecture instantiations will enhance the multi-domain and multi-technology integrated experimental network deployment with native ML optimization, energy-efficient aware slicing, and slicing-tailored security functionalities for the practical domain.
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