A Dynamic Weight Deep Reinforcement Learning Approach for SDN Multi-Objective Optimization with Actuator Integration

In recent years, the surge in network traffic has led to a substantial increase in energy consumption, making the construction of green and energy-efficient networks a critical challenge in the field of communications. Software-Defined Networking (SDN), with its centralized control characteristic, provides a new paradigm for the collaborative scheduling of actuators. However, traditional distributed network architectures lack global regulation capabilities, resulting in low resource utilization. Moreover, existing SDN traffic management methods mostly adopt fixed-weight reward functions, which are difficult to adapt to the dynamic fluctuation of network traffic and device heterogeneity, failing to meet the real-time and stability requirements of actuators in control scenarios. To address these issues, this study proposes a Dynamic Weight Generation Deep Q-Network (DWG-DQN) framework. By integrating a Long Short-Term Memory (LSTM) network with the SDN actuator scheduling mechanism, the system dynamically generates adaptive weight vectors, enabling real-time collaborative optimization of energy consumption, load balancing, and bandwidth utilization. Experimental results demonstrate that in fat-tree topology experiments, the proposed method achieves a 12.23% increase in average reward, a 33.93% reduction in energy consumption, a 31.12% improvement in load balancing, and a 24.03% enhancement in bandwidth utilization. Compared with fixed-weight method, it consistently outperforms in key performance indicators. The dynamic weight generation mechanism effectively solves the multi-objective optimization problem of actuators in dynamic network environments, offering a viable solution for the intelligent scheduling of actuators in SDN-based green traffic management.