TAQ:增强小包机制中的公平性和性能可预测性

Jay Chen, L. Subramanian, J. Iyengar, B. Ford
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引用次数: 13

摘要

TCP拥塞控制算法隐式地假设每流吞吐量是每次往返时间至少几个数据包。这种假设不成立的环境,我们称之为小数据包制度,在发展中地区的有线和蜂窝网络环境中很常见。在本文中,我们证明了在小数据包制度下,TCP流经历了严重的不公平,高丢包率和由于重复超时而导致的流沉默。我们提出了一个近似的马尔可夫模型来描述TCP在小数据包中的行为,以表征导致重复超时行为的TCP故障区域。为了在这种情况下提高TCP性能,我们提出了超时感知队列(TAQ),这是一种易于部署的网络中间盒方法,它使用多级自适应优先级队列算法来降低超时概率,提高公平性和性能可预测性。我们通过模拟、原型实现和测试平台实验,证明了TAQ在小数据包制度网络条件下的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TAQ: enhancing fairness and performance predictability in small packet regimes
TCP congestion control algorithms implicitly assume that the per-flow throughput is at least a few packets per round trip time. Environments where this assumption does not hold, which we refer to as small packet regimes, are common in the contexts of wired and cellular networks in developing regions. In this paper we show that in small packet regimes TCP flows experience severe unfairness, high packet loss rates, and flow silences due to repetitive timeouts. We propose an approximate Markov model to describe TCP behavior in small packet regimes to characterize the TCP breakdown region that leads to repetitive timeout behavior. To enhance TCP performance in such regimes, we propose Timeout Aware Queuing (TAQ), a readily deployable in-network middlebox approach that uses a multi-level adaptive priority queuing algorithm to reduce the probability of timeouts, improve fairness and performance predictability. We demonstrate the effectiveness of TAQ across a spectrum of small packet regime network conditions using simulations, a prototype implementation, and testbed experiments.
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