期刊名称：Journal of Computer Networks and Communications
印刷版ISSN：2090-7141
电子版ISSN：2090-715X
出版年度：2019
卷号：2019
DOI：10.1155/2019/4067135
出版社：Hindawi Publishing Corporation
摘要：Accurate real-time traffic prediction is required in many networking applications like dynamic resource allocation and power management. This paper explores a number of predictors and searches for a predictor which has high accuracy and low computation complexity and power consumption. Many predictors from three different classes, including classic time series, artificial neural networks, and wavelet transform-based predictors, are compared. These predictors are evaluated using real network traces. Comparison of accuracy and cost, both in terms of computation complexity and power consumption, is presented. It is observed that a double exponential smoothing predictor provides a reasonable tradeoff between performance and cost overhead.
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