DISCO: A Framework for Dynamic Selection of Multipath Congestion Control Algorithms

被引:0
|
作者
Yang, Furong [1 ,2 ,3 ]
Li, Zhenyu [1 ,2 ,4 ]
Zhou, Jianer [5 ]
Zhang, Xinyi [7 ]
Wu, Qinghua [1 ,2 ,4 ]
Pau, Giovanni [6 ]
Xie, Gaogang [2 ,7 ]
机构
[1] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
[2] Univ Chinese Acad Sci, Beijing, Peoples R China
[3] Sorbonne Univ, Paris, France
[4] Purple Mt Labs, Nanjing, Peoples R China
[5] Southern Univ Sci & Technol, Shenzhen, Peoples R China
[6] Univ Bologna, Bologna, Italy
[7] Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China
基金
国家重点研发计划; 北京市自然科学基金;
关键词
Congestion Control; MPTCP; Machine Learning; TCP;
D O I
10.1109/ICNP59255.2023.10355608
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Many mobile devices are usually equipped with multiple interfaces, providing the opportunity of using multipath transport protocols such as Multipath TCP (MPTCP) to boost performance. The multipath congestion control algorithm (CCA) in MPTCP plays a vital role in achieving high performance and multipath fairness in mobile environments where the paths are often heterogeneous and dynamic. Such environments are very challenging for existing one-size-fits-all CCAs to achieve high performance while ensuring multipath fairness. In this paper, we present a novel framework, Disco, to dynamically select the most appropriate CCAs for MPTCP subflows at runtime according to the perceived network condition. Extensive experiments show that compared with existing multipath CCAs, the proposed solution can improve the average throughput by 19% 25% and reduce the average queuing delay by up to 21% while it barely does harm to multipath fairness.
引用
收藏
页数:12
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