Simple and Accurate Identification of High-Rate Flows by Packet Sampling

N. Kamiyama, Tatsuya Mori
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引用次数: 40

Abstract

Unfairness among best-effort flows is a serious problem on the Internet. In particular, UDP flows or unresponsive flows that do not obey the TCP flow control mechanism can consume a large share of the available bandwidth. High-rate flows seriously affect other flows, so it is important to identify them and limit their throughput by selectively dropping their packets. As link transmission capacity increases and the number of active flows increases, however, capturing all packet information becomes more difficult. In this paper, we propose a novel method of identifying high-rate flows by using sampled packets. The proposed method simply identifies flows from which Y packets are sampled without timeout. The identification principle is very simple and the implementation is easy. We derive the identification probability for flows with arbitrary flow rates and obtain an identification curve that clearly demonstrates the accuracy of identification. The characteristics of this method are determined by three parameters: the identification threshold Y , the timeout coefficient K, and the sampling interval N . To match the experimental identification probability to the theoretical one and to simplify the identification mechanism, we should set K to the maximum allowable value. Although increasing Y improves the identification accuracy, both the required memory size and the processing power grow as Y increases. Numerical evaluation using an actual packet trace demonstrated that the proposed method achieves very high identification accuracy with a much simpler mechanism than that of previously proposed methods.
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基于分组采样的高速率流的简单准确识别
尽力流之间的不公平是互联网上的一个严重问题。特别是UDP流或不遵守TCP流控制机制的无响应流会占用大量可用带宽。高速率流严重影响其他流,因此识别它们并通过选择性地丢弃它们的数据包来限制它们的吞吐量非常重要。然而,随着链路传输容量的增加和活动流数量的增加,捕获所有数据包信息变得更加困难。在本文中,我们提出了一种利用采样包识别高速率流的新方法。所提出的方法只是简单地识别从其中采样Y数据包而不超时的流。该识别原理简单,易于实现。我们推导了任意流量下的识别概率,并得到了一条识别曲线,清晰地证明了识别的准确性。该方法的特性由三个参数决定:识别阈值Y、超时系数K和采样间隔N。为了使实验识别概率与理论识别概率相匹配,并简化识别机制,我们应该将K设置为最大允许值。虽然增加Y可以提高识别精度,但所需的内存大小和处理能力都会随着Y的增加而增加。利用实际数据包跟踪的数值计算表明,该方法比以前提出的方法具有更高的识别精度,且机制简单得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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