Deriving traffic demands for operational IP networks: methodology and experience
IEEE/ACM Transactions on Networking (TON)
Traffic matrix estimation: existing techniques and new directions
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Fast accurate computation of large-scale IP traffic matrices from link loads
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
An information-theoretic approach to traffic matrix estimation
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Traffic engineering with estimated traffic matrices
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
A distributed approach to measure IP traffic matrices
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Traffic matrices: balancing measurements, inference and modeling
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Traffic matrix tracking using Kalman filters
ACM SIGMETRICS Performance Evaluation Review - Special issue on the First ACM SIGMETRICS Workshop on Large Scale Network Inference (LSNI 2005)
Routing Inference Based on Pseudo Traffic Matrix Estimation
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 01
A fast lightweight approach to origin-destination IP traffic estimation using partial measurements
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
An independent-connection model for traffic matrices
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
OSPF monitoring: architecture, design and deployment experience
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
Gradually reconfiguring virtual network topologies based on estimated traffic matrices
IEEE/ACM Transactions on Networking (TON)
An assignment model on traffic matrix estimation
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Traffic Matrix Estimation Using Square Root Filtering/Smoothing Algorithm
APNOMS '08 Proceedings of the 11th Asia-Pacific Symposium on Network Operations and Management: Challenges for Next Generation Network Operations and Service Management
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The traffic matrix (TM) is one of the crucial inputs for 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 them by modeling incorporating measurable data and additional information. Many estimation methods have been proposed so far, but most of them are not sufficiently accurate or efficient. Researchers are therefore making efforts to seek better estimation methods. Of the proposed methods, the Kalman Filtering method is a very efficient and accurate method. However, the error covariance calculation components of Kalman filtering are difficult to implement in realistic network systems due to the existence of ill-conditioning problems. In this paper, we proposed a square root Kalman filtering traffic matrix estimation (SRKFTME) algorithm based on matrix decomposition to improve the Kalman filtering method. The SRKFTME algorithm makes use of the evolution equations of forecast and analysis error covariance square roots. In this way the SRKFTME algorithm can ensure the positive definiteness of the error covariance matrices, which can solve some ill-conditioning problems. Also, square root Kalman filtering will be less affected by numerical problems. Simulation and actual traffic testing results show superior accuracy and stability of SRKFTME algorithm compared with prior Kalman filtering methods.