Short-term MPEG-4 video traffic prediction using ANFIS
International Journal of Network Management
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Computer Networks: The International Journal of Computer and Telecommunications Networking
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CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
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WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Prediction of MPEG video source traffic using bilinear recurrent neural networks
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Multiscale bilinear recurrent neural network for prediction of MPEG video traffic
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Multimedia Tools and Applications
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Information Sciences: an International Journal
Small-time scale network traffic prediction based on flexible neural tree
Applied Soft Computing
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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Computer Communications
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Multimedia services and especially digital video is expected to be the major traffic component transmitted over communication networks [such as internet protocol (IP)-based networks]. For this reason, traffic characterization and modeling of such services are required for an efficient network operation. The generated models can be used as traffic rate predictors, during the network operation phase (online traffic modeling), or as video generators for estimating the network resources, during the network design phase (offline traffic modeling). In this paper, an adaptable neural-network architecture is proposed covering both cases. The scheme is based on an efficient recursive weight estimation algorithm, which adapts the network response to current conditions. In particular, the algorithm updates the network weights so that 1) the network output, after the adaptation, is approximately equal to current bit rates (current traffic statistics) and 2) a minimal degradation over the obtained network knowledge is provided. It can be shown that the proposed adaptable neural-network architecture simulates a recursive nonlinear autoregressive model (RNAR) similar to the notation used in the linear case. The algorithm presents low computational complexity and high efficiency in tracking traffic rates in contrast to conventional retraining schemes. Furthermore, for the problem of offline traffic modeling, a novel correlation mechanism is proposed for capturing the burstness of the actual MPEG video traffic. The performance of the model is evaluated using several real-life MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases. The results indicate that the proposed adaptable neural-network architecture presents better performance than other examined techniques.