xmlui.dri2xhtml.METS-1.0.item-contributor-funder:
European Commission
Sponsor:
The work of University Carlos III of Madrid was supported by the H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5GROWTH project (grant agreement no. 856709). The research of Marco Fiore was partially supported by the ANR CANCAN project (ANR-18-CE25-0011).
Network slicing is an emerging paradigm in mobile networks that leverages Network Function Virtualization (NFV) to enable the instantiation of multiple virtual networks -named slices- over the same physical network infrastructure. The operator can allocate to Network slicing is an emerging paradigm in mobile networks that leverages Network Function Virtualization (NFV) to enable the instantiation of multiple virtual networks -named slices- over the same physical network infrastructure. The operator can allocate to each slice dedicated resources and customized functions that allow meeting the highly heterogeneous and stringent requirements of modern mobile services. Managing functions and resources under network slicing is a challenging task that requires making efficient decisions at all network levels, in some cases even in real-time, which can be achieved by integrating artificial intelligence (AI) in the network. We outline a general framework for AI-based network slice management, introducing AI in the different phases of the slice lifecycle, from admission control to dynamic resource allocation in the network core and at the radio access. A sensible use of AI for network slicing results in strong benefits for the operator, with expected performance gains between 25% and 80% in representative case studies.[+][-]