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Original scientific paper
https://doi.org/10.2498/cit.1001989

Wavelet Transform-based Network Traffic Prediction: A Fast On-line Approach

Hong Zhao ; Fairleigh Dickinson University, Teaneck, USA
Nirwan Ansari ; New Jersey Institute of Technology, University Heights, Newark, USA

Fulltext: english, pdf (385 KB) pages 15-25 downloads: 630* cite
APA 6th Edition
Zhao, H. & Ansari, N. (2012). Wavelet Transform-based Network Traffic Prediction: A Fast On-line Approach. Journal of computing and information technology, 20 (1), 15-25. https://doi.org/10.2498/cit.1001989
MLA 8th Edition
Zhao, Hong and Nirwan Ansari. "Wavelet Transform-based Network Traffic Prediction: A Fast On-line Approach." Journal of computing and information technology, vol. 20, no. 1, 2012, pp. 15-25. https://doi.org/10.2498/cit.1001989. Accessed 16 Apr. 2021.
Chicago 17th Edition
Zhao, Hong and Nirwan Ansari. "Wavelet Transform-based Network Traffic Prediction: A Fast On-line Approach." Journal of computing and information technology 20, no. 1 (2012): 15-25. https://doi.org/10.2498/cit.1001989
Harvard
Zhao, H., and Ansari, N. (2012). 'Wavelet Transform-based Network Traffic Prediction: A Fast On-line Approach', Journal of computing and information technology, 20(1), pp. 15-25. https://doi.org/10.2498/cit.1001989
Vancouver
Zhao H, Ansari N. Wavelet Transform-based Network Traffic Prediction: A Fast On-line Approach. Journal of computing and information technology [Internet]. 2012 [cited 2021 April 16];20(1):15-25. https://doi.org/10.2498/cit.1001989
IEEE
H. Zhao and N. Ansari, "Wavelet Transform-based Network Traffic Prediction: A Fast On-line Approach", Journal of computing and information technology, vol.20, no. 1, pp. 15-25, 2012. [Online]. https://doi.org/10.2498/cit.1001989

Abstracts
High speed network traffic prediction is essential to provision QoS for multimedia applications while keeping bandwidth utilization high. Wavelet transform is a powerful technique for analyzing time domain signals. When combined with LMS, wavelet based predictor can achieve better performance than time domain predictor for MPEG-4 VBR videos and self-similar traffic. However, the computational complexity in predicting each wavelet coefficient is high. In this paper, LMK (Least Mean Kurtosis), which uses the negated kurtosis of the error signal as the cost function, is first proposed to estimate wavelet coefficients; then, by analyzing the wavelet coefficients of two consecutive data sets, Reduced Computation Complexity Wavelet LMK (RCCWLMK) is proposed to reduce the computational complexity. Simulation results for a wide range of MPEG-4 videos and network self-similar traffic show that RCCWLMK not only incurs smaller prediction error, but also reduces the computational complexity greatly.

Keywords
multiscale analysis; traffic prediction; and MPEG-4 videos

Hrčak ID: 84063

URI
https://hrcak.srce.hr/84063

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