信息统计流反演
Towards Informative Statistical Flow Inversion
摘要：
最近引起研究关注的一个问题是估计互联网流量中流量大小的分布。在高流量链路上，有时不可能记录每个数据包。研究人员已经以两种不同的方式处理了从采样的分组数据估计流长度的问题。首先，可以尝试不同的采样方法以更准确地测量期望的系统参数。一种这样的方法是采样保持方法，其中，如果分组被采样，则该流中的所有后续分组被采样。其次，统计方法可以用来“反演”采样数据，并从样本中估计流长。在本文中，我们提出，实施和测试两个变量的采样和保持方法。此外，我们展示了如何采样和保持方法可以被倒置，以获得真正的流量大小分布的估计。在真实的网络轨迹上进行实验，比较标准数据包采样与三种采样保持算法。的方法进行比较，他们的能力，重建流量大小的真正分布。
英文摘要：
A problem which has recently attracted research attention is that of estimating the distribution of flow sizes in internet traffic. On high traffic links it is sometimes impossible to record every packet. Researchers have approached the problem of estimating flow lengths from sampled packet data in two separate ways. Firstly, different sampling methodologies can be tried to more accurately measure the desired system parameters. One such method is the sample-and-hold method where, if a packet is sampled, all subsequent packets in that flow are sampled. Secondly, statistical methods can be used to ``invert'' the sampled data and produce an estimate of flow lengths from a sample. In this paper we propose, implement and test two variants on the sample-and-hold method. In addition we show how the sample-and-hold method can be inverted to get an estimation of the genuine distribution of flow sizes. Experiments are carried out on real network traces to compare standard packet sampling with three variants of sample-and-hold. The methods are compared for their ability to reconstruct the genuine distribution of flow sizes in the traffic.
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