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A Novel Flash P2P Network Traffic Prediction Algorithm based on ELMD and Garch

Author

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  • Yimu Ji

    (School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China†Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, Jiangsu 210023, China‡Institute of High-Performance Computing and Big Data, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China§Nanjing Center of HPC China, Nanjing, Jiangsu 210023, China¶Jiangsu HPC and Intelligent Processing Engineer Research Center, Nanjing, Jiangsu 210023, China)

  • Ye Wu

    (School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China)

  • Dianchao Zhang

    (School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China)

  • Yongge Yuan

    (School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China)

  • Shangdong Liu

    (School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China‡Institute of High-Performance Computing and Big Data, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China§Nanjing Center of HPC China, Nanjing, Jiangsu 210023, China¶Jiangsu HPC and Intelligent Processing Engineer Research Center, Nanjing, Jiangsu 210023, China)

  • Roozbeh Zarei

    (#x2225;School of Information Technology, Deakin University, Burwood, VIC 3125, Australia**Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Zhejiang, Ningbo, China)

  • Jing He

    (#x2020;†Institute of Information Technology, Nanjing University of Finance and Economics, Nanjing, Jiangsu 210023, China‡‡Software and Electrical Engineering, Swinburne University of Technology, Australia)

Abstract

To improve the quality of service and network performance for the Flash P2P video-on-demand, the prediction Flash P2P network traffic flow is beneficial for the control of the network video traffic. In this paper, a novel prediction algorithm to forecast the traffic rate of Flash P2P video is proposed. This algorithm is based on the combination of the ensemble local mean decomposition (ELMD) and the generalized autoregressive conditional heteroscedasticity (GARCH). The ELMD is used to decompose the original long-related flow into the summation of the short-related flow. Then, the GRACH is utilized to predict the short-related flow. The developed algorithm is tested in a university’s campus network. The predicted results show that our proposed method can further achieve higher accuracy than those obtained by existing algorithms, such as GARCH and Local Mean Decomposition and Generalized AutoRegressive Conditional Heteroskedasticity (LMD-GARCH) while keeping lower computational complexity.

Suggested Citation

  • Yimu Ji & Ye Wu & Dianchao Zhang & Yongge Yuan & Shangdong Liu & Roozbeh Zarei & Jing He, 2020. "A Novel Flash P2P Network Traffic Prediction Algorithm based on ELMD and Garch," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 127-141, March.
    • Handle: RePEc:wsi:ijitdm:v:19:y:2020:i:01:n:s0219622019500469
    DOI: 10.1142/S0219622019500469
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    References listed on IDEAS

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    1. Feng, Shuo & Wang, Xingmin & Sun, Haowei & Zhang, Yi & Li, Li, 2018. "A better understanding of long-range temporal dependence of traffic flow time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 639-650.
    2. Li, Ming & Lim, S.C., 2008. "Modeling network traffic using generalized Cauchy process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2584-2594.
    3. Sharpe, Jamie, 2019. "Re-evaluating the impact of immigration on the U.S. rental housing market," Journal of Urban Economics, Elsevier, vol. 111(C), pages 14-34.
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