Network slicing is a technique widely used in 5G networks where multiple logical networks (i.e., slices) run over a single shared physical infrastructure. Each slice may realize one or multiple services, whose specific requirements are negotiated beforehand and regulated through Service Level Agreements (SLAs). In Beyond 5G (B5G) networks it is envisioned that slices should be created, deployed, and managed in an automated fashion (i.e., without human intervention) irrespective of the technological and administrative domains over which a slice may span. Achieving this vision requires a combination of novel physical layer technologies, artificial intelligence tools, standard interfaces, network function virtualization, and software-defined networking principles. This paper provides an overview of the challenges facing network slicing automation with a focus on transport networks. Results from a selected group of use cases show the benefits of applying conventional optimization tools and machine-learning-based techniques while addressing some slicing design and provisioning problems.

Network Slicing Automation: Challenges and Benefits

Raffaelli C.;
2020

Abstract

Network slicing is a technique widely used in 5G networks where multiple logical networks (i.e., slices) run over a single shared physical infrastructure. Each slice may realize one or multiple services, whose specific requirements are negotiated beforehand and regulated through Service Level Agreements (SLAs). In Beyond 5G (B5G) networks it is envisioned that slices should be created, deployed, and managed in an automated fashion (i.e., without human intervention) irrespective of the technological and administrative domains over which a slice may span. Achieving this vision requires a combination of novel physical layer technologies, artificial intelligence tools, standard interfaces, network function virtualization, and software-defined networking principles. This paper provides an overview of the challenges facing network slicing automation with a focus on transport networks. Results from a selected group of use cases show the benefits of applying conventional optimization tools and machine-learning-based techniques while addressing some slicing design and provisioning problems.
2020
2020 24th International Conference on Optical Network Design and Modeling, ONDM 2020
1
6
Tonini F.; Natalino C.; Furdek M.; Raffaelli C.; Monti P.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/769754
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