GNN-Based Multi-Agent DRL for Energy-Efficient Multi-Domain 6G Resource Orchestration
Résumé
The rapid evolution towards 6G networks introduces new challenges in orchestrating services across distributed domains while ensuring sustainability goals, such as energy efficiency. Traditional scaling strategies focus on the number of network function instances without optimizing their placement based on energy consumption or resource usage. Addressing this gap, we propose a distributed and energy-efficient placement framework for scaled Network Function (NF) instances across multi-domain 6G infrastructures. Building upon a refined energy consumption model that accounts for computational and network-level power usage, we design a Graph Neural Network (GNN)-enhanced Deep Reinforcement Learning (DRL) agent to optimize placement decisions. The agent encodes the substrate topology and resource states to guide the selection of energyefficient nodes during scaling operations. We implement and evaluate the framework in a realistic multi-domain scenario featuring fluctuating traffic patterns and heterogeneous node energy profiles. Results show that our approach reduces infrastructure energy consumption compared to a round-robin heuristic, while maintaining high placement success and efficient resource utilization. Among the DRL methods explored, Proximal Policy Optimization (PPO) achieved the best trade-off between placement stability, adaptability, and energy performance. These findings demonstrate the potential of GNN-based DRL agents for sustainable orchestration in future 6G networks.
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