Anomaly-Tolerant Traffic Matrix Estimation via Prior Information Guided Matrix Completion

Network management, planning, and optimization rely on accurate and complete traffic measurement. However, anomalies and missing data are inevitable in direct traffic measurement because of high measurement costs and unreliable network transport protocols. Existing traffic matrix estimation approaches concern only the outlier and ignore the structural anomaly, which often degrades the estimation accuracy drastically. To address this challenge, an anomaly-tolerant traffic matrix estimation approach called Simultaneously Estimate Traffic Matrix and Detect Anomaly (SETMADA) is presented. By utilizing the prior low-rank property and temporal characteristic of the traffic matrix, the traffic matrix estimation in the coexistence of the outlier and structural anomaly is formulated as a Prior Information Guided Matrix Completion (PigMaC) problem, where outlier and structural anomalies are modeled by the L1-norm and L2,1-norm, respectively. Furthermore, by employing the multi-blocks ADMM and stochastic proximal gradient descent, a scalable parallel optimization algorithm PigMaC-ADMM-S is proposed to solve the PigMaC problem. To our knowledge, SETMADA is the first approach that can simultaneously estimate the missing traffic matrix and explicitly sift outlier and structural anomalies. Simulation results demonstrate that SETMADA achieves better estimation performance compared with the state-of-the-art algorithms. In addition, SETMADA provides an accurate localization of outlier and structural anomaly, which is the prerequisite for malfunction diagnosis in the large-scale networks.