Adaptive Linear Prediction Augmented Autoregressive Integrated Moving Average Based Prediction for VBR Video Traffic
Abstract
Problem statement: Network traffic prediction plays a vital role in the optimal resource allocation and management in computer networks. This study introduces an ARIMA based model augmented by Adaptive Linear Prediction (ALP) for the real time prediction of VBR video traffic. The synergy of the two can successfully address the challenges in traffic prediction such as accuracy in prediction, resource management and utilization. Approach: ARIMA application on a VBR video trace results in a component wise representation of the trace which is then used for prediction. The ALP applied afterwards, ensures consistency in error fluctuation and better accuracy in turn. Results: Performance evaluation of the proposed method is carried out using RMSE. The prediction accuracy is improved by 23% and the error variance is reduced by 23%. Conclusion: The performance of the proposed method is thoroughly investigated, by applying it on video traces of different qualities and characteristics.
DOI: https://doi.org/10.3844/jcssp.2011.871.876
Copyright: © 2011 T. Raghuveera, P. V.S. Kumar and K. S. Easwarakumar. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Traffic prediction
- Autoregressive Integrated Moving Average (ARIMA)
- VBR video
- Adaptive Linear Prediction (ALP)
- packet loss
- traffic characteristics
- video traffic
- prediction accuracy
- prediction process
- univariate time
- linear combination
- error variance