A Machine Learning-Based Framework for Dynamic Selection of Congestion Control Algorithms

doi:10.1109/TNET.2022.3220225

	A Machine Learning-Based Framework for Dynamic Selection of Congestion Control Algorithms
	Zhou, Jianer 1,2; Qiu, Xinyi 2; Li, Zhenyu 3; Li, Qing 2; Tyson, Gareth 4; Duan, Jingpu 2; Wang, Yi 1,2; Pan, Heng 3; Wu, Qinghua 3
	2022-11-16
发表期刊	IEEE-ACM TRANSACTIONS ON NETWORKING
ISSN	1063-6692
页码	16
摘要	Most congestion control algorithms (CCAs) are designed for specific network environments. As such, there is no known algorithm that achieves uniformly good performance in all scenarios for all flows. Rather than devising a one-size-fits-all algorithm (which is a likely impossible task), we propose a system to dynamically switch between the most suitable CCAs 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 the stack to use the most suitable CCA for individual flows. We build a machine learning model to learn which algorithm works best for individual conditions and implement kernel-level support for dynamically switching between CCAs. The framework also takes application requirements of performance into consideration to fine-tune the selection based on application-layer needs. Moreover, to reduce the overhead introduced by machine learning on individual front-end servers, we (optionally) implement the CCA selection process in the cloud, which allows the share of models and the selection among front-end servers. We have implemented Antelope in Linux, and evaluated it in both emulated and production networks. The results demonstrate the effectiveness of Antelope via dynamic adjusting the CCAs for individual flows. Specifically, Antelope achieves an average 16% improvement in throughput compared with BBR, and an average 19% improvement in throughput and 10% reduction in delay compared with CUBIC.
关键词	Congestion control eBPF machine learning
DOI	10.1109/TNET.2022.3220225
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000886730600001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/20307
专题	中国科学院计算技术研究所期刊论文
通讯作者	Li, Zhenyu; Li, Qing
作者单位	1.Southern Univ Sci & Technol, Shenzhen 518055, Peoples R China 2.Peng Cheng Lab, Shenzhen 518066, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China 4.Hong Kong Univ Sci & Technol Guangzhou, Guangzhou 510000, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Jianer,Qiu, Xinyi,Li, Zhenyu,et al. A Machine Learning-Based Framework for Dynamic Selection of Congestion Control Algorithms[J]. IEEE-ACM TRANSACTIONS ON NETWORKING,2022:16.
APA	Zhou, Jianer.,Qiu, Xinyi.,Li, Zhenyu.,Li, Qing.,Tyson, Gareth.,...&Wu, Qinghua.(2022).A Machine Learning-Based Framework for Dynamic Selection of Congestion Control Algorithms.IEEE-ACM TRANSACTIONS ON NETWORKING,16.
MLA	Zhou, Jianer,et al."A Machine Learning-Based Framework for Dynamic Selection of Congestion Control Algorithms".IEEE-ACM TRANSACTIONS ON NETWORKING (2022):16.