1 research outputs found
Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration
Network 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.Comment: 10 pages, 11 figure