PREDICTIVE MULTI-LAYER RESOURCE SLICING FOR 5G NETWORKS
Context. The rapid deployment of 5G networks and the emergence of 6G architectures introduce unprecedented traffic heterogeneity and burstiness across radio, edge, and core domains. Meanwhile, the energy footprint of mobile infrastructure is becoming a major sustainability concern, as carbon emissions increasingly shape network operation policies.Objective. This work aims to design a predictive carbon-aware multi-layer resource slicing framework for RAN – edge – core 5G/6G networks that jointly optimizes latency, cost, energy, and carbon emissions under bursty traffic conditions.Method. The proposed approach integrates an M/G/1-based queuing model for accurate representation of heavy-tailed service times and bursty arrival patterns; hybrid short-term/long-term forecasting of both traffic load and regional carbon intensity; and multi-objective optimization for carbon-aware VNF placement and traffic steering across network layers. A proactive – reactive orchestration mechanism performs predictive resource pre-allocation and runtime scaling.Results. Trace-driven simulations on a representative multi-layer testbed demonstrate a 34% reduction in CO2 emissions compared to latency-first orchestration, alongside a 22% decrease in operational cost and <1% SLA violation rate. Tail latency remains within slice-specific thresholds even under bursty loads, confirming that carbon reductions can be achieved without service degradation.Conclusions. Predictive, carbon-aware orchestration across RAN-edge-core domains substantially improves environmental and economic efficiency while preserving QoS guarantees. The results highlight the importance of integrating forecast-drivenoptimization and realistic traffic modeling into next-generation slicing architectures.