Inferring invisible traffic

V. Bharti, P. Kankar, L. Setia, Gonca Gürsun, Anukool Lakhina, M. Crovella
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引用次数: 26

Abstract

A traffic matrix encompassing the entire Internet would be very valuable. Unfortunately, from any given vantage point in the network, most traffic is invisible. In this paper we describe results that hold some promise for this problem. First, we show a new characterization result: traffic matrices (TMs) typically show very low effective rank. This result refers to TMs that are purely spatial (have no temporal component), over a wide range of spatial granularities. Next, we define an inference problem whose solution allows one to infer invisible TM elements. This problem relies crucially on an atomicity property we define. Finally, we show example solutions of this inference problem via two different methods: regularized regression and matrix completion. The example consists of an AS inferring the amount of invisible traffic passing between other pairs of ASes. Using this example we illustrate the accuracy of the methods as a function of spatial granularity.
推断不可见流量
一个包含整个互联网的流量矩阵将是非常有价值的。不幸的是,从网络中任何给定的有利位置来看,大多数流量都是不可见的。在本文中,我们描述了一些对这个问题有希望的结果。首先,我们展示了一个新的表征结果:流量矩阵(TMs)通常显示非常低的有效秩。这个结果是指在很大的空间粒度范围内,纯空间(没有时间成分)的tm。接下来,我们定义一个推理问题,其解决方案允许推断不可见的TM元素。这个问题主要依赖于我们定义的原子性。最后,我们通过正则化回归和矩阵补全两种不同的方法给出了该推理问题的示例解。该示例包括一个AS推断在其他AS对之间传递的不可见流量的数量。通过这个例子,我们说明了这些方法作为空间粒度函数的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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