密集WiFi场景下TCP下载性能

Mukulika Maity, B. Raman, Mythili Vutukuru
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引用次数: 9

摘要

密集WiFi网络的性能如何,特别是对于TCP下载的常见情况?虽然这个问题的经验答案是“差”,但先前工作的分析和实验表明,TCP本身的时钟相当好,避免了密集环境中竞争驱动的WiFi过载。本文的重点是在一个密集的场景中对WiFi的实际使用进行测量:一个教室里,几个学生使用网络下载测验和教学材料。我们发现TCP下载性能很差,这与之前的工作建议相反。通过仔细分析,我们解释了导致这种不良性能的各种现象的复杂相互作用。具体来说,我们观察到下载数据时产生的少量上传流量会扰乱TCP时钟,并增加通道上的争用。此外,争用丢失会导致与权威机构适应和TCP超时机制交互不良的恶性循环。为了减少信道争用并提高性能,我们提出了对AP调度策略的修改,以提高TCP大下载的性能。我们的解决方案WiFiRR在任何时刻都只选择AP服务的客户端子集,并在所有客户端上以循环的方式定期更改这组“活动”客户端,以确保没有客户端饿死。通过在任何时间点减少竞争节点的数量,WiFiRR在模拟我们的课堂场景中将大型TCP流的下载时间提高了3.2倍。
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TCP download performance in dense WiFi scenarios
How does a dense WiFi network perform, specifically for the common case of TCP download? While the empirical answer to this question is `poor', analysis and experimentation in prior work has indicated that TCP clocks itself quite well, avoiding contention-driven WiFi overload in dense settings. This paper focuses on measurements from a real-life use of WiFi in a dense scenario: a classroom where several students use the network to download quizzes and instruction material. We find that the TCP download performance is poor, contrary to that suggested by prior work. Through careful analysis, we explain the complex interaction of various phenomena which leads to this poor performance. Specifically, we observe that a small amount of upload traffic generated when downloading data upsets the TCP clocking, and increases contention on the channel. Further, contention losses lead to a vicious cycle of poor interaction with autorate adaptation and TCP's timeout mechanism. To reduce channel contention and improve performance, we propose a modification to the AP scheduling policy to improve the performance of large TCP downloads. Our solution, WiFiRR, picks only a subset of clients to be served by the AP during any instant, and varies this set of “active” clients periodically in a round-robin fashion over all clients to ensure that no client starves. By reducing the number of contending nodes at any point of time, WiFiRR improves the download time of large TCP flows by 3.2× in a simulation of our classroom scenario.
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