Baselining network-wide traffic by time-frequency constrained Stable Principal Component Pursuit

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.

An efficient and robust algorithm for BSS by maximizing reference-based negentropy

“…As shown in [11], [12], [24], the Newton method can be used to solve (10) and the Newton iteration is presented as follows:…”

“…M and N are the number of observations and sources, respectively. When the sources are linearly and instantaneously mixed, BSS corresponds to independent component analysis (ICA) [8][9][10]. In this paper, we mainly study the efficient optimization problem of BSS in the ICA framework.…”