The efficiency of flow-based networking mechanisms strongly depends on traffic characteristics and should thus be assessed using accurate flow models. For example, in the case of algorithms based on the distinction between elephant and mice flows, it is extremely important to ensure realistic flows’ length and size distributions. Credible models or data are not available in literature. Numerous works contain only plots roughly presenting empirical distribution of selected flow parameters, without providing distribution mixture models or any reusable numerical data. This paper aims to fill that gap and provide reusable models of flow length and size derived from real traffic traces. Traces were collected at the Internet-facing interface of the university campus network and comprise four billion layer-4 flow (275 TB). These models can be used to assess a variety of flow-oriented solutions under the assumption of realistic conditions. Additionally, this paper provides a tutorial on constructing network flow models from traffic traces. The proposed methodology is universal and can be applied to traffic traces gathered in any network. We also provide an open source software framework to analyze flow traces and fit general mixture models to them.

基于流的联网机制的效率在很大程度上取决于流量特性，因此应使用准确的流模型进行评估。例如，在基于大象和老鼠流之间的区分的算法的情况下，确保现实流的长度和大小分布极为重要。在文献中没有可靠的模型或数据。许多工作仅包含粗略显示选定流量参数的经验分布的图，而没有提供分布混合模型或任何可重复使用的数值数据。本文旨在填补这一空白，并提供可重用的，基于实际交通轨迹得出的流量长度和大小的模型。痕迹是在大学校园网络的面向Internet的界面上收集的，包含40亿个4层流（275 TB）。在实际条件下，这些模型可用于评估各种面向流动的解决方案。此外，本文提供了有关根据流量跟踪构造网络流模型的教程。所提出的方法是通用的，并且可以应用于在任何网络中收集的流量跟踪。我们还提供了一个开放源代码软件框架来分析流动痕迹并为它们拟合一般的混合物模型。