Abstract
Stretching across the horizon of data communication and networking, in almost every scenario, accurate bandwidth allocation has been a challenging problem. From simple online video streaming to the sophisticated communication network underlying a Smart Grid, efficient management of bandwidth is always desired. One way to achieve such efficiency lies in the science of predication: an intelligent system can be deployed that can estimate the sizes of upcoming data packets by analyzing patterns in the previously received data. This paper presents such a system that implements a fast and robust Neuro-Evolutionary algorithm known as Recurrent-Cartesian Genetic Programming evolved Artificial Neural Network (R-CGPANN). Based on the previously received 10 MPEG-4 video frames, the system estimates the size of the next frame. The simulation results show that the recurrence in CGPANN measurably outperform not only the feed forward version of the said algorithm but other contemporary methods in the field.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ullah, F., Khan, G.M., Mahmud, S.A.: Intelligent Bandwidth Management Using Fast Learning Neural Networks. In: 9th International Conference on High Performance Computing and Communication (HPCC-ICESS), pp. 867–872 (June 2012)
Yao, X.: Evolving Artificial Neural Networks. Proc. IEEE 87, 1423–1447 (1999)
Gomez, F., Schmidhuber, J., Miikkulainen, R.: Accelerated Neural Evolution Through Cooperatively Coevolved Synapses. J. Mach. Learn. Res. 9, 937–965 (2008)
Moriarty, D.: Symbiotic Evolution of Neural Networks in Sequential Decision Tasks. PhD thesis, University of Texas at Austin, Tech Rep. UT-A197-257 (1997)
Polani, D., Miikkulainen, R.: Eugenic Neuro-Evolution for Reinforcement Learning. In: GECCO, pp. 1041–1046 (2000)
Stanley, K.O., Miikkulainen, R.: Efficient reinforcement learning through evolving neural networks topologies. In: GECCO 2002 (2002)
Mayer, A., Mayer, H.A.: Multi-Chromosomal Representation in NeuroEvolution. In: Computations Intelligence Conference (2006)
Takagi, H.: Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation. Proc. of the IEEE 89(9), 1275–1296 (2001)
Schmidhuber, J., Wierstra, D., Gomez, F.: Evolino: Hybrid Neuroevolution Optimal Linear Search for Sequence Prediction. In: 19th Int. Conf. on Artificial Intelligence (2005)
Tseng, Y.H., Wu, E.H.-K., Chen, G.H.: Scene-Change Aware Dynamic Bandwidth Allocation for Real-Time VBR Video Transmission Over IEEE 802.15.3 Wireless Home Networks. IEEE Transactions on Multimedia 9(3), 642–654 (2007)
Kuo, W.K., Lien, S.Y.: Dynamic resource allocation for supporting real-time multimedia applications in IEEE 802.15.3 WPANs. IET Com. 3(1), 1–9 (2009)
Adas, A.M.: Using adaptive linear prediction to support real-time VBR video under RCBR network service model. IEEE Trans. on Networking 6(5), 635–644 (1998)
Lanfranchi, L.I., Bing, B.K.: MPEG-4 Bandwidth Prediction for Broadband Cable Networks. IEEE Transactions on Broadcasting 54(4), 741–751 (2008)
Mangharam, R., Demirhan, M., Rajkumar, R., Raychaudhuri, D.: Size matters: Size-Based Scheduling for MEPG-4 over wireless channels. In: SPIE & ACM Proceedings in Multimedia Computing and Networking, vol. 3020, pp. 110–122 (January 2004)
Doulamis, A.D., Doulamis, N.D., Kollias, S.D.: An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources. IEEE Transactions on Neural Networks 14(1), 150–166 (2003)
Park, D.-C., Tran, C.N., Song, Y.-S., Lee, Y.: Prediction of MPEG video source traffic using biLinear recurrent neural networks. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 298–307. Springer, Heidelberg (2006)
Aninda, B., Parlos, A.G., Amir, A.F.: Prediction of MPEG-coded video source traffic using recurrent neural networks. IEEE Trans. Signal Processing 51(8), 2177–2190 (2003)
Jibukumar, M.G., Datta, R., Kumar, P.: Kalman filter based Variable Bit Rate video frame size prediction. In: 3rd Int. Symp. on Wireless Pervasive Computing, pp. 459–463 (May 2008)
Trlin, G.: VBR video frame size prediction using seasonal ARIMA. In: 20th Int. Conference on Software, Telecommunication and Computer Networks (SoftCOM), pp. 1–5 (September 2012)
Miller, J.F., Thomson, P.: Cartesian Genetic Programming. In: EuroGP, pp. 121–132 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Muhammad Khan, G., Ullah, F., Mahmud, S.A. (2013). MPEG-4 Internet Traffic Estimation Using Recurrent CGPANN. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-41013-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41012-3
Online ISBN: 978-3-642-41013-0
eBook Packages: Computer ScienceComputer Science (R0)