IP flows have heavy-tailed packet and byte size distributions. This make them poor candidates for uniform sampling---i.e. selecting 1 in N flows---since omission or inclusion of a large flow can have a large effect on estimated total traffic. Flows selected in this manner are thus unsuitable for use in usage sensitive billing. We propose instead using a size-dependent sampling scheme which gives priority to the larger contributions to customer usage. This turns the heavy tails to our advantage; we can obtain accurate estimates of customer usage from a relatively small number of important samples.The sampling scheme allows us to control error when charging is sensitive to estimated usage only above a given base level. A refinement allows us to strictly limit the chance that a customers estimated usage will exceed their actual usage. Furthermore, we show that a secondary goal, that of controlling the rate at which samples are produced, can be fulfilled provided the billing cycle is sufficiently long. All these claims are supported by experiments on flow traces gathered from a commercial network.
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机译：IP流具有重尾的数据包和字节大小分布。因此，他们不适合进行统一抽样，即在 N 个流量中选择1个---因为忽略或包含大流量会对估计的总流量产生很大的影响。因此，以这种方式选择的流程不适合用于对使用敏感的计费。我们建议改用大小相关的抽样方案，该方案优先考虑对客户使用的较大贡献。这使沉重的尾巴对我们有利。我们可以从相对较少的重要样本中获得对客户使用情况的准确估算。当计费仅对给定基准水平以上的估算使用情况敏感时，抽样方案才能控制错误。改进使我们能够严格限制客户估计使用量超出其实际使用量的机会。此外，我们显示，只要开票周期足够长，就可以实现控制产量的第二个目标。所有这些主张都得到了从商业网络收集到的流动轨迹实验的支持。
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