Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation

Abstract

The rapid growth of high-quality telecommunications demands enhanced queueing system performance. Traditional bandwidth distribution often struggles to adapt to dynamic changes, network conditions, and erratic traffic patterns. Internet traffic fluctuates over time, causing resource underutilization. To address these challenges, this paper proposes a new adaptive algorithm called Weighted Fair Queues continual Deep Reinforcement Learning (WFQ continual-DRL), which integrates the advanced deep reinforcement learning Soft Actor-Critic (SAC) algorithm with the Elastic Weight Consolidation (EWC) approach. This technique is designed to overcome neural networks’ catastrophic forgetting, thereby enhancing network routers’ dynamic bandwidth allocation. The agent is trained to allocate bandwidth weights for multiple queues dynamically by interacting with the environment to observe queue lengths. The performance of the proposed adaptive algorithm was evaluated for eight queues until it expanded to twelve-queue systems. The model achieved higher cumulative rewards as compared to previous studies, indicating improved overall performance. The values of the Mean Squared Error (MSE) and Mean Absolute Error (MAE) decreased, suggesting effectively optimized bandwidth allocation. Reducing Root Mean Square Error (RMSE) indicated improved prediction accuracy and enhanced fairness computed by Jain’s index. The proposed algorithm was validated by employing real-world network traffic data, ensuring a robust model under dynamic queuing requirements.