Achieving Flexible and Lightweight Multipath Congestion Control Through Online Learning
Achieving Flexible and Lightweight Multipath Congestion Control Through Online Learning
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DOI：
10.1109/tnsm.2022.3208747
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发表时间：
2023-03
影响因子：
5.3
通讯作者：
Rui Zhuang;Jiangping Han;Kaiping Xue;Jian Li;David S. L. Wei;Ruidong Li;Qibin Sun;Jun Lu
中科院分区：
文献类型：
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作者：
Rui Zhuang;Jiangping Han;Kaiping Xue;Jian Li;David S. L. Wei;Ruidong Li;Qibin Sun;Jun Lu
网络设备升级为配备多个网络接口，使得通过多路径传输协议，特别是多路径传输协议(MPTCP)来提高网络吞吐量性能成为可能。然而，到目前为止，大多数使用的MPTCP协议都有一个共同的局限性，那就是方法僵化和保守。它们在设计时几乎没有考虑到真实网络是动态的，网络状态经常变化，从而导致当前的MPTCP在许多现实场景中性能不佳。本文提出了一种基于在线学习的轻量级多路径拥塞控制算法MP-OL。MP-OL将拥塞控制建模为多臂强盗问题，并通过在线学习灵活自适应地调整各子流的发送速率。因此，MP-OL具有适应各种网络场景的能力，能够在动态的网络环境中实现公平性和高性能。它还可以灵活地在在线学习和传统方法之间切换，在保证学习效率的同时降低了计算复杂度，从而使MP-OL易于部署和使用。实验结果表明，与主流的MPTCP协议相比，MP-OL在公平性和链路利用率方面都有显著的提高，对非拥塞丢失具有更好的恢复能力，对不稳定的网络环境具有更好的适应性。在实际网络中，MP-OL也获得了更好的吞吐量性能。
The upgrade of network devices to be equipped with multiple network interfaces makes it possible to improve network throughput performance through multipath transmission protocols, especially multipath TCP (MPTCP). However, so far the mostly used MPTCP protocols have a common limitation, namely the rigid and conservative method. They have been designed with little consideration of the fact that real networks are dynamic and the network status changes frequently, thus leading to the poor performance of current MPTCP in many realistic scenarios. In this paper, we propose a lightweight multipath congestion control algorithm based on online learning, named MP-OL. MP-OL models congestion control as a multi-armed bandit problem, and adjusts the sending rate of each subflow flexibly and adaptively through online learning. Therefore, MP-OL possesses the capability of suiting various network scenarios, and can achieve fairness and high performance in dynamic network environment. It can also flexibly switch between online learning and traditional method, which reduces the computational complexity while ensuring the learning efficiency, thus making MP-OL easy to deploy and use. As the experimental results demonstrated, compared with the leading MPTCP variants, MP-OL achieves significant improvements in fairness and link utilization, and shows better resilience to non-congestion loss and better adaptability to unstable network conditions. In real networks, MP-OL also obtains better throughput performance.