Accurate real-time traffic prediction is required in many networking applications like dynamic resource allocation and power\nmanagement. This paper explores a number of predictors and searches for a predictor which has high accuracy and low\ncomputation complexity and power consumption. Many predictors from three different classes, including classic time series,\nartificial neural networks, and wavelet transform-based predictors, are compared. These predictors are evaluated using real\nnetwork traces. Comparison of accuracy and cost, both in terms of computation complexity and power consumption, is presented.\nIt is observed that a double exponential smoothing predictor provides a reasonable tradeoff between performance and\ncost overhead.
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