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LearningCC: An online learning approach for congestion control
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2021-08-02 , DOI: 10.1002/ett.4331 Songyang Zhang 1 , Weimin Lei 1, 2 , Wei Zhang 1 , Yunchong Guan 3
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2021-08-02 , DOI: 10.1002/ett.4331 Songyang Zhang 1 , Weimin Lei 1, 2 , Wei Zhang 1 , Yunchong Guan 3
Affiliation
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It is noticed that traditional loss based algorithms are not moving data very well in today's Internet. To get bottleneck bandwidth fully utilized, these algorithms will keep increasing the congestion window and maintain a standing queue of packets in routers, which cause large end to end latency. Such long latency is quite annoying to these ever growing delay sensitive applications. Recently, much effort has been devoted by researchers from both academia and industry to design better congestion control protocols and make a better Internet. In this article, we present an online learning based approach named LearningCC for congestion control. Instead of adjusting the congestion window with fixed rule as these traditional algorithms do, there are several options for an endpoint to choose. Each option is mapped as an arm of a bandit machine. The exploration and exploitation scheme is used to guide the selection on congestion window update rule. Through trial and error, an endpoint improves its performance by choosing action with better reward. Experiments are conducted on ns3 platform to verify the effectiveness of LearningCC. Results indicate it achieves lower transmission delay than loss based algorithms. Especially, we found LearningCC makes significant throughput improvement in link suffering from random loss.
中文翻译：
LearningCC：一种用于拥塞控制的在线学习方法
值得注意的是，传统的基于损失的算法在当今的互联网中不能很好地移动数据。为了充分利用瓶颈带宽，这些算法将不断增加拥塞窗口并在路由器中保持数据包的站立队列，从而导致较大的端到端延迟。对于这些不断增长的延迟敏感应用程序来说，如此长的延迟非常烦人。最近，学术界和工业界的研究人员付出了很多努力来设计更好的拥塞控制协议并打造更好的互联网。在本文中，我们提出了一种名为 LearningCC 的基于在线学习的拥塞控制方法。不像这些传统算法那样用固定规则调整拥塞窗口，端点有多种选项可供选择。每个选项都被映射为老虎机的一个臂。探索开发方案用于指导拥塞窗口更新规则的选择。通过反复试验，端点通过选择具有更好奖励的动作来提高其性能。在ns3平台上进行实验以验证LearningCC的有效性。结果表明它比基于损耗的算法实现了更低的传输延迟。特别是，我们发现 LearningCC 在遭受随机丢失的链路中显着提高了吞吐量。
更新日期：2021-08-02
中文翻译：
LearningCC：一种用于拥塞控制的在线学习方法
值得注意的是，传统的基于损失的算法在当今的互联网中不能很好地移动数据。为了充分利用瓶颈带宽，这些算法将不断增加拥塞窗口并在路由器中保持数据包的站立队列，从而导致较大的端到端延迟。对于这些不断增长的延迟敏感应用程序来说，如此长的延迟非常烦人。最近，学术界和工业界的研究人员付出了很多努力来设计更好的拥塞控制协议并打造更好的互联网。在本文中，我们提出了一种名为 LearningCC 的基于在线学习的拥塞控制方法。不像这些传统算法那样用固定规则调整拥塞窗口，端点有多种选项可供选择。每个选项都被映射为老虎机的一个臂。探索开发方案用于指导拥塞窗口更新规则的选择。通过反复试验，端点通过选择具有更好奖励的动作来提高其性能。在ns3平台上进行实验以验证LearningCC的有效性。结果表明它比基于损耗的算法实现了更低的传输延迟。特别是，我们发现 LearningCC 在遭受随机丢失的链路中显着提高了吞吐量。
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