Efficient Prediction of Network Traffic for Real-Time Applications
Joint Authors
Iqbal, Muhammad Faisal
Zahid, Muhammad
Habib, Durdana
John, Lizy Kurian
Source
Journal of Computer Networks and Communications
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-04
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Iqbal, Muhammad Faisal& Zahid, Muhammad& Habib, Durdana& John, Lizy Kurian. 2019. Efficient Prediction of Network Traffic for Real-Time Applications. Journal of Computer Networks and Communications،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1172259
Modern Language Association (MLA)
Iqbal, Muhammad Faisal…[et al.]. Efficient Prediction of Network Traffic for Real-Time Applications. Journal of Computer Networks and Communications No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1172259
American Medical Association (AMA)
Iqbal, Muhammad Faisal& Zahid, Muhammad& Habib, Durdana& John, Lizy Kurian. Efficient Prediction of Network Traffic for Real-Time Applications. Journal of Computer Networks and Communications. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1172259
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1172259