Learning in the recurrent random neural network
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Traffic matrix estimation: existing techniques and new directions
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Experience in measuring backbone traffic variability: models, metrics, measurements and meaning
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Structural analysis of network traffic flows
Proceedings of the joint international conference on Measurement and modeling of computer systems
Traffic matrices: balancing measurements, inference and modeling
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Providing public intradomain traffic matrices to the research community
ACM SIGCOMM Computer Communication Review
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)
Sensitivity of PCA for traffic anomaly detection
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Large-Scale IP Traffic Matrix Estimation Based on Backpropagation Neural Network
ICINIS '08 Proceedings of the 2008 First International Conference on Intelligent Networks and Intelligent Systems
A study of real-time packet video quality using random neural networks
IEEE Transactions on Circuits and Systems for Video Technology
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Despite a large body of literature and methods devoted to the Traffic Matrix (TM) estimation problem, the inference of traffic flows volume from aggregated data still represents a major issue for network operators. Directly and frequently measuring a complete TM in a large-scale network is costly and difficult to perform due to routers limited capacities. In this paper we introduce and evaluate a new method to estimate a TM from easily available link load measurements. The method uses a novel statistical learning technique to unveil the relation between links traffic volume and origin-destination flows volume. By training a system based on Random Neural Networks, we provide a fast and accurate TM estimation tool that attains proper results without assuming any traffic model or particular behavior. Using real data from an operational backbone network, we compare this new method to the most well known and accepted TM estimation techniques, including in the evaluation some more accurate and up-to-date methods developed in recent works. Results show that current TM estimation techniques can still be improved.