Network Tomography and Partial Least Squares for Traffic Matrix Estimation

Abstract

The traffic matrix is a useful data structure in network management, monitoring, optimization and traffic forecast. A recurrent problem is to obtain accurate traffic matrices in real time from the traffic of a network, specially when this network is large (e.g., a Tier 1 Internet Service Provider), and without causing a relevant overhead in network computing, storage and communication resources. A solution deeply investigated in the past is the network tomography: the estimation of a traffic matrix from the volume of traffic traversing the links (a.k.a. link counts), which measurement implies a minimum overhead. This estimation entails relevant challenges. In this paper, we propose the application of the Partial Least Squares method to this problem. We illustrate the proposal with the Abilene network dataset, and report promising results in comparison to traditional methods like General Tomogravity and the Structural Analysis based on Principal Component Analysis.

Generative Deep Learning Techniques for Traffic Matrix Estimation From Link Load Measurements

“…More recently, the authors in [26] leverage the Partial Least Squares (PLS) regression technique to create a predictive model between the TM and the link counts. They also propose a suitable re-calibration strategy after fixed time intervals to maintain adequate performance.…”