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Learn more, sample less: control of volume and variance in network measurement

机译：了解更多，更少样本：控制网络测量中的数量和方差

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This paper deals with sampling objects from a large stream. Each object possesses a size, and the aim is to be able to estimate the total size of an arbitrary subset of objects whose composition is not known at the time of sampling. This problem is motivated from network measurements in which the objects are flow records exported by routers and the sizes are the number of packet or bytes reported in the record. Subsets of interest could be flows from a certain customer or flows from a worm attack. This paper introduces threshold sampling as a sampling scheme that optimally controls the expected volume of samples and the variance of estimators over any classification of flows. It provides algorithms for dynamic control of sample volumes and evaluates them on flow data gathered from a commercial Internet Protocol (IP) network. The algorithms are simple to implement and robust to variation in network conditions. The work reported here has been applied in the measurement infrastructure of the commercial IP network. To not have employed sampling would have entailed an order of magnitude greater capital expenditure to accommodate the measurement traffic and its processing.
机译：本文涉及从大量流中采样对象。每个对象都有一个大小，目的是能够估计采样时其组成未知的任意对象子集的总大小。此问题是由网络测量引起的，在网络测量中，对象是路由器导出的流记录，大小是记录中报告的数据包或字节数。感兴趣的子集可能是某个客户的流量，也可能是蠕虫攻击的流量。本文介绍阈值采样作为一种采样方案，该采样方案可以在任何流分类上最优地控制预期的样本量和估计量的方差。它提供了动态控制样品量的算法，并根据从商业互联网协议（IP）网络收集的流量数据对它们进行了评估。该算法易于实现，并且对于网络条件的变化具有鲁棒性。此处报告的工作已应用于商业IP网络的测量基础结构中。如果不采用抽样方法，将需要增加一个数量级的资本支出，以适应测量流量及其处理。

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