AI-Driven Framework for Scalable Management of Network Slices
Published in Unknown Venue, 2023
Recommended citation: Luis Blanco and Sławomir Kukliński and Engin Zeydan and Farhad Rezazadeh and Ashima Chawla and Lanfranco Zanzi and Francesco Devoti and Robert Kolakowski and Vasiliki Vlahodimitropoulou and Ioannis Chochliouros and Anne-Marie Bosneag and Sihem Cherrared and Luis A. Garrido and Sergio Barrachina-Muñoz and Josep Mangues (2023). AI-Driven Framework for Scalable Management of Network Slices. Unknown Venue. https://ieeexplore.ieee.org/abstract/document/10328194/
Abstract: This article describes a scalable solution for orchestrating and managing a massive number of network slices that leverages Artificial Intelligence (AI) techniques to design robust and sustainable networks. To achieve this goal, the proposed approach decomposes the management and orchestration (M&O) plane using separation of concerns and uses AI techniques to automate M&O operations. The M&O automation is achieved through the use of multiple, distributed and AI-driven control loops. The control loops have different goals and may work on the node level, slice level, inter-slice level or orchestration domain level. We also present a case study of using the proposed distributed intelligent components to scale, optimize and improve the network infrastructure. Finally, we briefly describe some challenges and future directions for scalable M&O on the road to 6G.