Traffic Matrix Estimation Using Square Root Filtering/Smoothing Algorithm

Authors:
Jingjing Zhou;Jiahai Yang;Yang Yang;Guanqun Zhang
Affiliations:
Information Engineering School, University of Science and Technology Beijing, Beijing, China PA 100083 and The Network Research Center, Tsinghua University, and Tsinghua National Laboratory for In ...;The Network Research Center, Tsinghua University, and Tsinghua National Laboratory for Information Science and Technology, Beijing, China PA 100084;Information Engineering School, University of Science and Technology Beijing, Beijing, China PA 100083;The Network Research Center, Tsinghua University, and Tsinghua National Laboratory for Information Science and Technology, Beijing, China PA 100084
Venue:
APNOMS '08 Proceedings of the 11th Asia-Pacific Symposium on Network Operations and Management: Challenges for Next Generation Network Operations and Service Management
Year:
2008

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Abstract

The traffic matrix (TM) is one of the crucial inputs in many network management and traffic engineering tasks. As it is usually impossible to directly measure traffic matrices, it becomes an important research topic to infer traffic matrix by reasonably modeling, and incorporating the measurement data of limited links, as well as other additional information. In this paper, we propose Square Root Filtering/Smoothing traffic matrix estimation (SRFsTME) algorithm based on Kalman Smoothing decomposition to improve our proposed Square Root Kalman Filtering traffic matrix estimation (SRKFTME) algorithm. Simulation and actual traffic testing results show that SRFsTME algorithm is more numerical accurate and stable than the SRKFTME algorithm.