Baselining Network-Wide Traffic by Time-Frequency Constrained Stable Principal Component Pursuit

TL;DR

This paper introduces SPCP-TFC, a novel traffic matrix decomposition method that incorporates time-frequency constraints to more accurately extract baseline network traffic patterns, outperforming existing methods in simulations.

Contribution

The paper proposes a new SPCP-TFC model with time-frequency constraints and an efficient algorithm, improving baseline traffic extraction accuracy over existing schemes.

Findings

SPCP-TFC outperforms RBL and PCA in simulations.

The model effectively captures smoothness in baseline traffic.

The algorithm demonstrates computational efficiency.

Abstract

The Internet traffic analysis is important to network management,and extracting the baseline traffic patterns is especially helpful for some significant network applications.In this paper, we study on the baseline problem of the traffic matrix satisfying a refined traffic matrix decomposition model,since this model extends the assumption of the baseline traffic component to characterize its smoothness, and is more realistic than the existing traffic matrix models. We develop a novel baseline scheme, named Stable Principal Component Pursuit with Time-Frequency Constraints (SPCP-TFC), which extends the Stable Principal Component Pursuit (SPCP) by applying new time-frequency constraints. Then we design an efficient numerical algorithm for SPCP-TFC. At last, we evaluate this baseline scheme through simulations, and show it has superior performance than the existing baseline schemes RBL and PCA.