Fairness Improvement of Congestion Control with Reinforcement Learning

抄録

With fast deployment of high speed wireless access networks, communication environments for internet access have been changing drastically. According to these wide range of network environments, a lot of TCP variants have been proposed. Each of these algorithms focuses on the specific environment and is designed with hardwired logic. This means there is no one-size-fits-all congestion control which can adapt to all environments. To resolve this problem, reinforcement learning based congestion control which learns operation suitable for each environment has been proposed. QTCP (Q-learning Based TCP) is one of the promising learning based TCPs. In this paper, we first reveal that a QTCP flow only behaves in the selfish manner of just increasing its own utility function, which causes unfairness between resource sharing flows. We propose a new QTCP congestion window control mechanism which is based on AIMD. Performance evaluation results show our proposal improves fairness without degrading high throughput and low latency characteristics of QTCP.