基于小波域混合高斯模型的自相似流量合成算法

自相似流量特性对网络性能具有重要影响，流量建模与合成是网络性能评价的基本环节．提出了一种基于小波域混合高斯模型的自相似流量建模与合成方法：小波变换的近似Karhunen—Loeve（K-L）变换特性可以有效去除流量过程的长程相关，而混合高斯模型准确地描述了小波系数的非高斯分布．对合成流量进行了统计分析以及排队性能仿真．实验表明该方法能够更准确地对通信流量进行建模和合成，并且具有运算量小（O（N））、流量生成快速等优点．

Self-Similar Traffic Synthesizing Using Gaussian Mixture Model in Wavelet Domain

It has been recognized that self-similarity of the Internet traffic significantly affects the performance of networks, and traffic modeling and generation is a primary step of network performance evaluation. An algorithm for self-similar traffic modeling and generation based on a mixture Gaussian model in wavelet domain is proposed in this paper. The approximate Karhunen- Lo`eve transformation inherence endows wavelet with the power of decorrelating long-range dependence, and the mixture Gaussian model exactly captures the non-Gaussian distribution of wavelet coefficients. Both statistical analysis and queueing performance simulation are conducted to evaluate the proposed method. Numerical results suggest that this method can model and synthesize actual network traffic more accurately and has the advantages of low computation complexity of traffic generation in particular.