Efficient Prediction of Network Traffic for Real-Time Applications. (4th February 2019)

• Record Type: Journal Article
• Authors: Iqbal, Muhammad Faisal; Zahid, Muhammad; Habib, Durdana; John, Lizy Kurian
• Other Names: Xu Zhiyong Academic Editor.
• Is Part Of: Journal of computer networks and communications. Volume 2019(2019)
• Publication Date: 2019-02-04
• Subjects: Computer networks -- Periodicals; Computer science -- Periodicals; 004.605
• DOI: 10.1155/2019/4067135
• Languages: English
• ISSNs: 2090-7141

Abstract

Abstract : 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.