基于小波的网络连接层流量模型

最近研究表明网络流量的统计特性在不同尺度上表现出很大的差异,由大量具有高斯性的网络流量和少量的突发性网络流量组成.为了更好地理解这种复杂的网络流量统计特性,引入多尺度分析工具小波,在多尺度分析的基础上提出一种新的基于小波的网络连接层流量模型,有机地结合了独立小波模型和多重分形小波模型的优点,即保持了独立小波模型在不同尺度对高斯性方差的拟合,同时保持了多重分形小波模型对突发性的拟合.真实网络流量的仿真结果表明这个模型能够灵活地适应网络流量在不同尺度下所表现出的高斯特性和突发行为.

Connection-level network traffic model based on wavelet

Recent research shows network traffic exhibits drastically different statistics according to scales, including one component holding most traffic and being mostly Gaussian and the other absorbing virtually all the small scale bursts. For better understanding of this phenomenon, wavelet for multiscale analysis was introduced, a novel traffic model based on wavelet was proposed. Independent wavelet model was proved to be an ideal model for the Gaussian component, while the bursty component find a better match with the multifractal wavelet model, this novel traffic model combined the merits of independent wavelet model and multifractal wavelet model. Simulation results with the real traffic show this model is flexible and parsimonious to accommodate Gaussian as well as bursty behavior on different scales.