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.

中文翻译：

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一种基于ELMD和Garch的Flash P2P网络流量预测算法

为了提高Flash P2P视频点播的服务质量和网络性能，预测Flash P2P网络流量有利于网络视频流量的控制。本文提出了一种新颖的预测Flash P2P视频流量率的预测算法。该算法基于集成局部均值分解 (ELMD) 和广义自回归条件异方差 (GARCH) 的组合。ELMD 用于将原始的长相关流分解为短相关流的总和。然后，利用 GRACH 预测短相关流。开发的算法在一所大学的校园网络中进行了测试。预测结果表明，我们提出的方法可以进一步达到比现有算法更高的精度，