Antelope: A Framework for Dynamic Selection of Congestion Control Algorithms

Zhou，Jianer1,2; Qiu，Xinyi2; Li，Zhenyu2,3; Tyson，Gareth4; Li，Qing2; Duan，Jingpu1,2; Wang，Yi1,2

10.1109/ICNP52444.2021.9651912

2021

1092-1648

978-1-6654-3096-8

Proceedings - International Conference on Network Protocols, ICNP

2021-November

1-11

1-5 Nov. 2021

Dallas, TX, USA

Most congestion control mechanisms are designed for specific network environments. Hence, there is no known algorithm that achieves uniformly good performance in all scenarios for all flows. Rather than devising such a one-size-fits-all algorithm, we propose a system to dynamically switch between the most suitable congestion control mechanisms for specific flows in specific environments. This raises a number of challenges, which we address through the design and implementation of Antelope, a system that can dynamically reconfigure to use the most suitable congestion control mechanism for an individual flow. We build a machine learning approach to learn which algorithm works best for individual conditions and implement kernel-level support for dynamically adjusting congestion control algorithms. We have implemented Antelope in Linux, and evaluated it in both emulated and production networks. We show that in WAN, DCN, and cellular networks, Antelope achieves an average 16% improvement in throughput compared with BBR; compared with Cubic, Antelope achieves an average 19% improvement in throughput and 10% reduction in delay.

Wide area networks Machine learning algorithms Protocols Heuristic algorithms Linux Switches Production

第一

英语

CPCI-S ; EI

WOS:000749316400005

20220611610577

Computer operating systems

2-s2.0-85122604252

Scopus

1.Southern University of Science and Technology,Shenzhen,China
2.Peng Cheng Laboratory,Shenzhen,China
3.ICT,CAS,China
4.Queen Mary University of London,United Kingdom

Zhou，Jianer,Qiu，Xinyi,Li，Zhenyu,et al. Antelope: A Framework for Dynamic Selection of Congestion Control Algorithms[C],2021:1-11.