DRL-ABS: Deep Reinforcement Learning-Based Adaptive Buffer Sizing for Edge Routers in AIoT Networks

The proliferation of edge Internet of Things (IoT) devices intensifies congestion challenges in edge IoT networks. Adjusting edge router buffer sizes is a common and effective solution to reduce queuing delay and mitigate congestion. However, designing an optimal buffer sizing strategy that effectively balances low queuing delay with high throughput and minimal packet loss remains a key challenge. To address this, this paper proposes DRL-ABS, an adaptive buffer sizing method based on deep reinforcement learning. DRL-ABS employs a Dueling DQN model to implement its strategy, effectively controlling buffer size to reduce queuing delay. First, we introduce a novel congestion determination mechanism to enhance decision stability against transient traffic fluctuations. Then, we design a reward function based on the congestion status to balance packet loss rate and throughput. Finally, we construct a NS3-PyTorch joint simulation framework to bridge the gap between NS-3 and AI algorithms. Simulation results demonstrate that, compared to static buffer sizing, traditional adaptive buffer sizing, and AI-dreiven adaptive buffer sizing, DRL-ABS significantly reduces the average buffer size, average queuing delay, and jitter while maintaining a high average throughput and a comparable total packet loss rate. Furthermore, DRL-ABS exhibits good stability, maintaining a high average buffer utilization rate across different congestion control systems.