Reinforcement Learning-Based In-Network Load Balancing
Hesam Tajbakhsh, Ricardo Parizotto, Alberto Egon Schaeffer-Filho, Israat Haque · IEEE Transactions on Network and Service Management · 2025
Ensuring consistent performance becomes increasingly challenging with the growing complexity of applications in data centers. This is where load balancing emerges as a vital component. A load balancer distributes network or application traffic across various servers, resources, or pathways. In this article, we present P4WISE, a load balancer designed for software-defined networks. Operating on both the data and control planes, it employs reinforcement learning to distribute computational loads with granularity at inter and intra-server levels. Evaluation results demonstrate a remarkable 90% accuracy in predicting the optimal load balancing strategy of P4WISE in dynamic scenarios. Notably, unlike supervised or unsupervised methods, it eliminates the need for retraining when the environment undergoes minor or major changes. Instead, P4WISE autonomously adjusts and retrains itself based on observed states within the data center.