Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration

Abstract

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 endto-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.

Security Threats, Requirements and Recommendations on Creating 5G Network Slicing System: A Survey

“…Under the premise of massive data guarantee, 5G network slicing scenarios are gradually receiving attention from AI-related research and applications, and the potential of using machine learning means to ensure the security of slicing is also increasingly evident. AI and ML technologies manage, enhance and distribute network slices within 5G networks, a process known as AI/ML-assisted network slicing [55,56]. As technologies advance, mechanisms based on reinforcement learning are being developed to ensure the security of network slices [57].…”