Congestion Control in SDN-Based Networks via Multi-Task Deep Reinforcement Learning

Abstract

Congestion control is a fundamental network task that modulates the data transmission rates of traffic sources to efficiently utilize network capacity. With the advent of machine learning, congestion control based on deep reinforcement learning is the subject of extensive attention. At present, research on machine-learning-based congestion control is mainly focused on single-task scenarios -- to design a control strategy that is only used to reduce congestion. This article studies the congestion control problem based on machine learning in multi-task scenarios. Specifically, we propose a congestion control model based on multi-task deep reinforcement learning. The model takes congestion control as the main task and load balancing as the auxiliary task. Compared to the single-task method, our model can better represent the network environment by learning the shared representation of congestion features and load balancing features. Moreover, network traffic control may involve both congestion control and load balancing. Therefore, learning multiple tasks jointly while exploiting commonalities and differences across tasks can help reduce the cost of task coordination. We use software defined networking to decouple the data and control planes, making network control strategies more flexible than traditional networks. To the best of our knowledge, this is the first time multi-task learning has been applied to network traffic control. Experimental results show that the method is efficient.