Efficient Prediction of Network Traffic for Real-Time Applications
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 predicto...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/2019/4067135 |
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| _version_ | 1832562053985861632 |
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| author | Muhammad Faisal Iqbal Muhammad Zahid Durdana Habib Lizy Kurian John |
| author_facet | Muhammad Faisal Iqbal Muhammad Zahid Durdana Habib Lizy Kurian John |
| author_sort | Muhammad Faisal Iqbal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6f89e9ea606f480f87b01bcc637f2a0e |
| institution | Kabale University |
| issn | 2090-7141 2090-715X |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj-art-6f89e9ea606f480f87b01bcc637f2a0e2025-02-03T01:23:33ZengWileyJournal of Computer Networks and Communications2090-71412090-715X2019-01-01201910.1155/2019/40671354067135Efficient Prediction of Network Traffic for Real-Time ApplicationsMuhammad Faisal Iqbal0Muhammad Zahid1Durdana Habib2Lizy Kurian John3Capital University of Science and Technology, Islamabad, PakistanCentre of Excellence in Science and Applied Technologies, Islamabad, PakistanNational University of Computer and Emerging Sciences, Islamabad, PakistanThe University of Texas at Austin, Austin, TX, USAAccurate 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.http://dx.doi.org/10.1155/2019/4067135 |
| spellingShingle | Muhammad Faisal Iqbal Muhammad Zahid Durdana Habib Lizy Kurian John Efficient Prediction of Network Traffic for Real-Time Applications Journal of Computer Networks and Communications |
| title | Efficient Prediction of Network Traffic for Real-Time Applications |
| title_full | Efficient Prediction of Network Traffic for Real-Time Applications |
| title_fullStr | Efficient Prediction of Network Traffic for Real-Time Applications |
| title_full_unstemmed | Efficient Prediction of Network Traffic for Real-Time Applications |
| title_short | Efficient Prediction of Network Traffic for Real-Time Applications |
| title_sort | efficient prediction of network traffic for real time applications |
| url | http://dx.doi.org/10.1155/2019/4067135 |
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