How to reconstruct end-to-end traffic based on time-frequency analysis and artificial neural network

作者：Jiang Dingde^*; Zhao Zuyao; Xu Zhengzheng; Yao Chunping; Xu Hongwei

来源：AEU-International Journal of Electronics and Communications, 2014, 68(10): 915-925.

DOI：10.1016/j.aeue.2014.04.011

• Network traffic measurement
• End-to-end traffic
• Traffic modeling
• Time-frequency analysis
• Traffic reconstruction

End-to-end traffic, which describes the inherent characteristics and end-to-end behaviors of communication networks, is the crucial input parameter of network management and network traffic engineering. This paper proposes a new reconstruction algorithm to develop the research on reconstruction of end-to-end traffic in large-scale communication networks. We firstly conduct the time-frequency analysis on end-to-end traffic, and then localize its features to gain its time-frequency properties before decomposing it into the low-frequency and high-frequency components. We find that if decomposing appropriately, the low-frequency component of end-to-end traffic can accurately reflect its change trend, while its high-frequency component can well show the burst and fluctuation nature. This motivates us to find a reasonable time-frequency decomposition strategy to extract the low-frequency and high-frequency components of end-to-end traffic. Moreover, this further inspires us to use the regressive model to model the low-frequency part, exploit artificial neural network to characterize the high-frequency component, and then combine these two parts according to the regressive model and artificial neural network to precisely reconstruct end-to-end traffic. Simulation results show that in contrast to previous methods our algorithm is much more effective and promising.

• 出版日期2014
• 单位东北大学