Zero-touch AI-driven distributed management for energy-efficient 6G massive network slicing

Chergui, Hatim; Blanco, Luis; Garrido, Luis A; Ramantas, Kostas; Kuklinski, Slawomir; Ksentini, Adlen; Verikoukis, Christos
IEEE Network, Vol.35, N°6, November/December 2021

Sixth-generation (6G) systems are expected to inaugurate the era of massive and extremely heterogeneous network slicing, where tenancy would be extended to the final consumer with the proliferation of advanced and diverse digital services. This introduces significant challenges to network management and orchestration in terms of scalability and sustainability. This article starts by elaborating on the architectural and artificial-in-telligence-based algorithmic designs to achieve energy efficiency (EE) in 6G, as well as analyzing their trade-offs. The article then introduces a novel statistical federated learning (StFL)-based analytic engine for zero-touch 6G massive network slicing, which performs slice-level resource prediction by learning in an offline fashion while respecting some preset long-term service level agreement (SLA) constraints defined in terms of the empirical cumulative distribution function and the percentile statistics, and hence uses a new proxy-Lagrangian two-player strategy to solve the local non-convex federated learning task without settling for surro-gates only. This guarantees 20 × lower service-lev-el agreement (SLA) violation rate with respect to the federated averaging (FedAvg) scheme, while achieving more than 10 × EE gain compared to an SLA-constrained centralized deep learning algorithm, which paves the way to sustainable mas-sive network slicing. Finally, the key open research directions in this emerging area are identified.

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