摘要
当前被广泛使用的分形高斯噪声法对高斯噪声功率谱逼近效果较差,生成自相似业务量精度不高。针对该问题,提出一种高次方线性拟合方法。将分形高斯噪声的功率谱分成有限项和无穷累加项2个部分,采用Matlab的拟合工具箱cftool对功率谱的无穷项进行高次拟合,减少运算量,克服以往采用数学推导过程复杂的问题,弥补数学推导过程中使用连续积分代替离散求和时造成的误差。仿真结果表明,改进方法生成自相似业务量的相对误差比采用公式推导的方式低,用小波法估计时相对误差降低到0.05%,生成自相似业务量的速度较快,生成长度为220的自相似序列用时0.634 s。
Currently widely used fractal Gaussian noise method is less effective in approximating Gaussian power spectrum and with low accuracy in generating self-similar traffic. To solve this problem,this paper proposes a high-order linear fitting method. Dividing the power spectrum of fractal Gaussian noise into two parts, which are finite and infinite accumulation terms,it uses the fitting toolbox cftool of Matlab to fit the infinite terms of power spectrum, reduces the amount of calculation, overcomes the complicated problems in mathematical deduction process, and makes up the error caused by using continuous integration instead of discrete summation in the process of mathematical derivation. Simulation results show that the relative error of the improved method is lower than that of the formula derivation method in generating self-similar traffic. The relative error is reduced to 0.05% by using wavelet method, and the computing speed of the improved method is fast. It takes 0. 634 seconds to generate the self-similar sequence with the length of 22^20.
出处
《计算机工程》
CAS
CSCD
北大核心
2016年第7期54-58,共5页
Computer Engineering
基金
国家部委基金资助项目
西南科技大学重点科研平台专职科研创新团队建设基金资助项目(tdtk02)
西南科技大学研究生创新基金资助项目(15ycx117)
关键词
自相似性
网络业务量
网络建模
分形高斯噪声
赫斯特参数
self-similarity
network traffic
network modeling
fractal Gaussian noise
Hurst parameter
