Traffic matrix estimation: existing techniques and new directions
Traffic matrix estimation: existing techniques and new directions
复制标题
DOI：
10.1145/633025.633041
复制
发表时间：
2002-10
期刊：
影响因子：
--
通讯作者：
A. Medina;N. Taft;Kave Salamatian;S. Bhattacharyya;C. Diot
中科院分区：
文献类型：
--
作者：
A. Medina;N. Taft;Kave Salamatian;S. Bhattacharyya;C. Diot
很少有技术已经提出了估计流量矩阵的背景下，互联网流量。我们在POP到POP流量矩阵（TM）方面的工作有两个贡献。主要的贡献是一个详细的比较评估现有的三种技术的结果。我们评估这些方法的估计误差，所需的先验信息的敏感性和敏感性，他们作出的统计假设。我们研究了路径长度和链路共享量等特性对估计误差的影响。使用实际数据从一级骨干，我们评估的有效性所需的TM估计技术的典型假设。我们的工作的第二个贡献是建议一个新的方向TM估计的基础上使用选择模型模型POP扇出。这些模型使我们能够克服现有方法的一些问题，因为它们可以包含有关持久性有机污染物的额外数据和信息，并且它们使我们能够做出一种根本不同的建模假设。我们验证这种方法，说明我们的建模假设匹配实际的互联网数据。使用两个初始的简单模型，我们提供了一个概念证明，表明POP功能（如总传入字节，客户数等）的知识的结合。可以减少估计误差。我们提出的方法可以与现有或未来的方法结合使用，因为它可以用来生成良好的先验，作为统计推断技术的输入。
Very few techniques have been proposed for estimating traffic matrices in the context of Internet traffic. Our work on POP-to-POP traffic matrices (TM) makes two contributions. The primary contribution is the outcome of a detailed comparative evaluation of the three existing techniques. We evaluate these methods with respect to the estimation errors yielded, sensitivity to prior information required and sensitivity to the statistical assumptions they make. We study the impact of characteristics such as path length and the amount of link sharing on the estimation errors. Using actual data from a Tier-1 backbone, we assess the validity of the typical assumptions needed by the TM estimation techniques. The secondary contribution of our work is the proposal of a new direction for TM estimation based on using choice models to model POP fanouts. These models allow us to overcome some of the problems of existing methods because they can incorporate additional data and information about POPs and they enable us to make a fundamentally different kind of modeling assumption. We validate this approach by illustrating that our modeling assumption matches actual Internet data well. Using two initial simple models we provide a proof of concept showing that the incorporation of knowledge of POP features (such as total incoming bytes, number of customers, etc.) can reduce estimation errors. Our proposed approach can be used in conjunction with existing or future methods in that it can be used to generate good priors that serve as inputs to statistical inference techniques.