A parallel low rank matrix optimization method for recovering internet traffic network data via link flow measurement

Internet traffic network data recovery via link flow measurement is an important problem in wireless communication network, but as the problem scale grows rapidly, it is quite challenging to give consideration to both recovery accuracy and computational cost. We construct a novel parallel low-rank matrix optimization model to accurately and quickly recover internet traffic network data via link flow measurement. This model takes full advantage of the low-rankness of traffic network data. Then, an inexact symmetric Gauss-Seidel-based majorized semi-proximal alternating direction method of multipliers is proposed to solve the model. Our method is proved to be globally convergent, and the numerical experiments on the classical Abilene and GeANT datasets indicate that the performance of our method for fast and accurate recovery of traffic network data is better than that of the state-of-the-art methods. Specially, the numerical results on the large-scale HOD dataset demonstrates that our method is quite suitable for traffic network data recovery problem from realistic scenarios.& COPY; 2023 Elsevier B.V. All rights reserved.

Automated summary

Internet traffic network data recovery via link flow measurement is a challenging problem in wireless communication network due to the rapid growth of problem scale. In this study, a novel parallel low-rank matrix optimization model is constructed to accurately and quickly recover internet traffic network data. The proposed method outperforms the state-of-the-art methods in terms of recovery accuracy and computational cost, as demonstrated by numerical experiments on various datasets.