论文标题
LearningCC：一种用于拥塞控制的在线学习方法
LearningCC: An online learning approach for congestion control
论文作者
论文摘要
最近，来自学术界和行业的研究人员为开发新颖的拥塞控制方法所做的很多努力。这封信中介绍了LearningCC，其中通过增强学习方法解决了拥堵控制问题。与其使用固定策略调整拥塞窗口，不如选择端点的服务选项。预测最佳选择是一项艰巨的任务。每个选项都映射为土匪机器的臂。端点可以通过反复试验来学会确定最佳选择。实验是在NS3平台上进行的，以通过与其他基准算法进行比较来验证LearningCC的有效性。结果表明，与基于损耗的算法相比，它可以实现较低的传输延迟。特别是，我们发现LearningCC在随机损失的链接上有了重大改进。
Recently, much effort has been devoted by researchers from both academia and industry to develop novel congestion control methods. LearningCC is presented in this letter, in which the congestion control problem is solved by reinforce learning approach. Instead of adjusting the congestion window with fixed policy, there are serval options for an endpoint to choose. To predict the best option is a hard task. Each option is mapped as an arm of a bandit machine. The endpoint can learn to determine the optimal choice through trial and error method. Experiments are performed on ns3 platform to verify the effectiveness of LearningCC by comparing with other benchmark algorithms. Results indicate it can achieve lower transmission delay than loss based algorithms. Especially, we found LearningCC makes significant improvement in link suffering from random loss.
