TCP 流量的多尺度分析

来源版块:网络流量管理
压缩包内文件格式:电子书
附件来源:互联网
运行平台:Windows平台
是否经本人验证:是
附件性质:免费
详细说明:TCP流量的多尺度分析
摘要:基于小波技术对多组局域网和广域网流量的实测数据进行了全局缩放性质和局部缩放性质的研究。讨论
了分形高斯噪声模型和多分形小波模型。在全局缩放性质的研究中,讨论了一类Hurst参数估计器,并将实际数
据与分形高斯噪声模型相比较,发现大时间尺度上的网络流量可以用分形高斯噪声来近似。在局部缩放性质的
研究中,分析了局部缩放指数及相应的结构函数,并将实际数据与多分形小波模型相比较,发现在小时间尺度
上,无论是分形高斯噪声还是多分形小波模型都不能很好地近似网络流量,大小时间尺度的划分在典型的往返
时间附近。
关键词:小波;缩放性质;分形高斯噪声;多分形;网络流量建模
本资料共包含以下附件：

(Source section: network traffic management
File format in compressed package: e-book
Attachment source: Internet
Running platform: Windows platform
Whether it has been verified by me: Yes
Nature of accessories: Free
Multi scale analysis of TCP traffic
Absrtact: Based on wavelet technology, the global scaling and local scaling properties of multi group measured data of LAN and WAN traffic are studied. discuss
Fractal Gaussian noise model and multifractal wavelet model are proposed. In the study of global scaling properties, a class of Hurst parameter estimators is discussed
Compared with fractal Gaussian noise model, it is found that network traffic on large time scale can be approximated by fractal Gaussian noise. In the local scaling property
In the research, the local scaling index and the corresponding structure function are analyzed, and the actual data are compared with the multifractal wavelet model
In fact, neither fractal Gaussian noise nor multifractal wavelet model can well approximate network traffic, and the division of large and small time scales is in a typical round trip
Around time.
Keywords: wavelet; Scaling properties; Fractal Gaussian noise; Multifractal; Network traffic modeling
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