Learning Based Methods for Traffic Matrix Estimation From Link Measurements

• Published in: IEEE open journal of the Communications Society Vol. 2; pp. 488 - 499
• Main Authors: Xu, Shenghe, Kodialam, Murali, Lakshman, T. V., Panwar, Shivendra S.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomalies; Communications traffic; Correlation; Current measurement; Estimation; Gallium nitride; Generative adversarial networks; Iterative methods; machine learning; Privacy; Regulation; Traffic capacity; Traffic matrix; Traffic planning
• ISSN: 2644-125X; 2644-125X
• DOI: 10.1109/OJCOMS.2021.3062636

Network traffic matrix (TM) is a critical input for capacity planning, anomaly detection and many other network management related tasks. The TMs are often computed from link load measurements. The TM estimation problem is the determination of the TM from link load measurements. The relationship between the link loads and the TM that generated the link loads can be modeled as an under-determined linear system and has multiple feasible solutions. Therefore, prior knowledge of the traffic demand pattern has to be used in order to find a potentially feasible TM. In this paper, we consider the TM estimation problem with limited prior information. Unlike previous methods that require past measurements of complete TMs, which are hard to obtain or protected by regulations, our method works even if only the distribution of TMs is known. We develop an iterative projection based algorithm to solve this problem. If large number of past TMs can be measured, we propose a Generative Adversarial Network (GAN) based approach for solving the problem. We compare the strengths of the two approaches and evaluate their performance for several networks using varying amounts of past data.