【作者】 胡俊；

【导师】 谭献海；

【作者基本信息】 西南交通大学 ， 计算机应用技术， 2008， 硕士

【摘要】 大量研究结果表明实际网络流量具有明显的尺度特性,在大尺度上表现为自相似(单分形),在小尺度上表现为多重分形。多重分形为刻画流量在小尺度上的奇异性提供了良好的数学框架。而小波变换对具有长程依赖性的流量起到了去相关的作用,因此有必要利用小波技术来研究多重分形。同时网络流量的多尺度特性也需要研究人员利用新的方法来探讨流量本质特征。流量的多重分形特性对网络性能有着非常重要的影响,建立一个基于多重分形特性的实际流量的业务模型很有必要。本文首先从实际网络流量的特征入手,分析研究流量的全局尺度和局部尺度特性,确定流量的分形特性与产生分形的时间,并且比较不同类型的流量在不同尺度下所表现出的性能,分析了产生这种现象的原因。为了深入研究在多重分形条件下影响实际流量特性的因素,以小波变换为基础,采取对实际流量分组的方法,分别进行组内打乱和组问打乱,发现均值和方差对多重分形有较大影响。其次,利用小波变换能够去除实际流量相关性的特点,结合实际研究过程中所发现的不同类型的流量具有不同的分形特性的结论,建立新的合成模型,提高合成流量的精度,并且通过对合成后的流量进行尺度刻画和性能评价,验证了新模型的正确性。最后,对建立一个基于多重分形特性的可以同时预报长相关和短相关特性的实际网络业务模型的必要性进行讨论,针对AR、ARMA等模型对短相关数据能较好的预测而对长相关数据预测精度不高的特点,并结合小波变换能够去除实际数据相关性的优势,建立新的预测模型,该预测模型对长相关数据同样具有比较高的预测精度。同时,改进后的模型也克服了FARIMA模型计算量比较大的缺点,保持了算法的简单性。更多还原

【Abstract】 A great deal of research results indicate that actual network traffic processes exhibit ubiquitous properties of multi-scales, namely self-similarity (mono-fractal) in large time scale and multi-fractal in small time scale. Multi-fractal offers a good mathematical framework to describe the singularity of traffic in small time scale. It is necessary to use wavelet transform to study the multi-fractal because of the decorrelation characteristic of Wavelet. The multi-scale characteristic of network traffic also needs to introduce new methods to study its essential characteristic.The multi-fractal characteristic of traffic has great effect on the performance of network. It is necessary to construct traffic model based on multi-fractal characteristic. Firstly, with the analysis of the characteristics of whole and local scale of actual network traffic, this paper ascertains the fractal characteristic and its initial time of traffic, and compares the different performance of traffic in different time scale, and analyzes the reason for the occurrence of this phenomena. In order to deeply study the factors which affect the characteristic of actual traffic under the condition of multi-fractal, the author using wavelet transform to analyze the dependence of the actual traffic, he divides the traffic into blocks and then internally or externally shuffles them. The test results show that mean and variance of the traffic both have great impact on multi-fractal.Secondly, based on the multi-fractal characteristic of the traffic and the property of decorrelation of wavelet transform, a novel synthetic model is constructed to improve the synthetic accuracy. Through the scale description and performance assessment of the synthetic traffic, the validation of the novel model is verified.Finally, the necessary of the construction of network traffic model based on multi-fractal characteristic is discussed. The model can predict long-range and short-range dependence of the actual traffic. Based on the characteristic that AR and ARMA models can correctly predict short-range dependence while has low prediction accuracy to long-range dependence, a novel prediction model is constructed based on the superiority of decorrelation of wavelet transform. This novel model has also high prediction accuracy to long-range dependence. At the same time, the improved model also conquers the defect of computing complication of FARIMA model and keeps the briefness of algorithm.更多还原