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
Structural analysis of network traffic flows
Proceedings of the joint international conference on Measurement and modeling of computer systems
How to identify and estimate the largest traffic matrix elements in a dynamic environment
Proceedings of the joint international conference on Measurement and modeling of computer systems
Traffic matrix estimation on a large IP backbone: a comparison on real data
Proceedings of the 4th 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
Estimating dynamic traffic matrices by using viable routing changes
IEEE/ACM Transactions on Networking (TON)
Oblivious routing of highly variable traffic in service overlays and IP backbones
IEEE/ACM Transactions on Networking (TON)
Redundancy in network traffic: findings and implications
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Swing: realistic and responsive network traffic generation
IEEE/ACM Transactions on Networking (TON)
Fine Two-Phase Routing with Traffic Matrix
ICCCN '09 Proceedings of the 2009 Proceedings of 18th International Conference on Computer Communications and Networks
GARCH model-based large-scale IP traffic matrix estimation
IEEE Communications Letters
Design and performance analysis of a practical load-balanced switch
IEEE Transactions on Communications
Describing network traffic using the index of variability
IEEE/ACM Transactions on Networking (TON)
Gradually reconfiguring virtual network topologies based on estimated traffic matrices
IEEE/ACM Transactions on Networking (TON)
Global modeling of backbone network traffic
INFOCOM'10 Proceedings of the 29th conference on Information communications
Tracking long duration flows in network traffic
INFOCOM'10 Proceedings of the 29th conference on Information communications
Generalized Regression Neural Networks With Multiple-Bandwidth Sharing and Hybrid Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized regression neural networks in time-varying environment
IEEE Transactions on Neural Networks
A general regression neural network
IEEE Transactions on Neural Networks
Density-Driven Generalized Regression Neural Networks (DD-GRNN) for Function Approximation
IEEE Transactions on Neural Networks
A power laws-based reconstruction approach to end-to-end network traffic
Journal of Network and Computer Applications
A compressive sensing-based reconstruction approach to network traffic
Computers and Electrical Engineering
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Traffic matrix (TM) is a key input of traffic engineering and network management. However, it is significantly difficult to attain TM directly, and so TM estimation is so far an interesting topic. Though many methods of TM estimation are proposed, TM is generally unavailable in the large-scale IP backbone networks and is difficult to be estimated accurately. This paper proposes a novel method of TM estimation in large-scale IP backbone networks, which is based on the generalized regression neural network (GRNN), called GRNN TM estimation (GRNNTME) method. Firstly, building on top of GRNN, we present a multi-input and multi-output model of large-scale TM estimation. Because of the powerful capability of learning and generalizing of GRNN, the output of our model can sufficiently capture the spatio-temporal correlations of TM. This ensures that the estimation of TM can accurately be attained. And then GRNNTME uses the procedure of data posttreating further to make the output of our model closer to real value. Finally, we use the real data from the Abilene Network to validate GRNNTME. Simulation results show that GRNNTME can perform well the accurate and fast estimation of TM, track its dynamics, and holds the stronger robustness and lower estimation errors.