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, cloud and verticals like the Internet of Vehicles industrial IoT, among others. NS foreseen as one leading enablers for 6G futuristic highly demanding applications since it allows optimization customization scarce disputed resources dynamic, clients with distinct application requirements. Various standardization organizations, 3GPP’s proposal new generation state-of-the-art 5G/6G research projects, are proposing architectures. However, architectures have to deal extensive range requirements that inherently result having architecture proposals typically fulfilling needs specific sets domains commonalities. The Future Infrastructures (SFI2) explores gap resulting from diversity target by a reference defined focus on integrating experimental enhancing Machine Learning (ML) native optimizations, energy-efficient slicing, slicing-tailored security functionalities. SFI2 architectural main contribution includes utilization slice-as-a-service paradigm end-to-end orchestration across multi-domains multi-technology networks. In addition, instantiations will enhance multi-domain integrated network deployment ML optimization, aware functionalities practical domain.