AI-Driven NFV Service Chaining Across the Edge-to-Cloud Continuum: Placement, Fairness, and Coordination

AI-driven Network Function Virtualization (NFV) service chaining is emerging as a key enabler for automation in edge-to-cloud infrastructures. However, existing standards and orchestration frameworks lack mechanisms for fairness, coordination, and dynamic resource sharing across distributed AI agents. This paper proposes a hierarchical architecture that integrates adaptive agent placement and fairness-aware scheduling for AI-based NFV coordination. At its core, the Multi-Agent Dynamic Bandwidth Environment (MADBE) framework leverages deep reinforcement learning to enable agents to collaboratively allocate shared bandwidth while meeting latency and throughput constraints. Experimental results demonstrate that MADBE significantly improves convergence speed, reduces conflict rates, and maintains bandwidth utilization near the 90% threshold, outperforming state-of-the-art baselines. Moreover, MADBE sustains near-zero violation rates for service-level constraints, even under dynamic and heterogeneous workloads. These results highlight the potential of fairness-aware, multi-agent coordination in next-generation 5G/6G networks and industrial automation environments.