Several network operations and management functions in the Internet depend on the traffic volume data represented as a traffic matrix (TM). Due to the difficulty in obtaining the TM data directly, several estimation techniques have been proposed in the literature. Most of the state-of-the-art techniques use subspace learning method that takes either one view of the data or multiple views gathered from different sources, to improve estimation accuracy. In this paper, we propose a multi-view subspace learning approach for accurate TM estimation using canonical correlation analysis. We define a TM view and show how multiple views of a TM, estimated with inexpensive techniques, can be used to estimate robust TMs. With experiments on Abilene network data, we show that the TMs estimated with the proposed multi-view learning technique have very low spatial and temporal error, compared to the other state-of-the-art techniques. We also show that the bias and variance in the estimated TMs are close to zero, which means that the estimated TMs can be very effective in capacity planning.

中文翻译：

---

一种用于互联网流量矩阵估计的多视图子空间学习方法

Internet 中的若干网络操作和管理功能依赖于表示为流量矩阵 (TM) 的流量数据。由于难以直接获得 TM 数据，文献中提出了几种估计技术。大多数最先进的技术使用子空间学习方法，该方法采用数据的一个视图或从不同来源收集的多个视图，以提高估计精度。在本文中，我们提出了一种多视图子空间学习方法，用于使用典型相关分析进行准确的 TM 估计。我们定义了一个 TM 视图，并展示了如何使用廉价技术估计的 TM 的多个视图来估计稳健的 TM。通过对 Abilene 网络数据的实验，我们表明，与其他最先进的技术相比，使用所提出的多视图学习技术估计的 TM 具有非常低的空间和时间误差。我们还表明，估计 TM 的偏差和方差接近于零，这意味着估计 TM 在容量规划中非常有效。