A Machine Learning-Based Framework for Dynamic Selection of Congestion Control Algorithms
文献类型：期刊论文
| 作者 | Zhou, Jianer3,4; Qiu, Xinyi3; Li, Zhenyu2; Li, Qing3; Tyson, Gareth1; Duan, Jingpu3; Wang, Yi3,4; Pan, Heng2; Wu, Qinghua2 |
| 刊名 | IEEE-ACM TRANSACTIONS ON NETWORKING
 |
| 出版日期 | 2022-11-16 |
| 页码 | 16 |
| 关键词 | Congestion control eBPF machine learning |
| ISSN号 | 1063-6692 |
| DOI | 10.1109/TNET.2022.3220225 |
| 英文摘要 | 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. |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000886730600001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/20306]  |
| 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.Hong Kong Univ Sci & Technol Guangzhou, Guangzhou 510000, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China 3.Peng Cheng Lab, Shenzhen 518066, Peoples R China 4.Southern Univ Sci & Technol, Shenzhen 518055, 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. |
入库方式： OAI收割
来源：计算技术研究所
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