A Multi-View Subspace Learning Approach to Internet Traffic Matrix Estimation

Abstract

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.