摘要
网络流量建模是网络规划与性能评价的重要基础,传统的业务模型大多基于泊松模型和马尔可夫排队模型,只具有短程相关性,随着网络业务的不断研究发现,实际网络业务流在很长的时间范围内都具有长程相关性,即一种自相似性。本文采用RMD算法和Fourier变换法对网络流量的自相似模型-FBM模型进行了建模及仿真研究,生成了所需的自相似流量序列。然后分别采用R/S法和方差时间图法对其进行自相似参数检测。结果验证了仿真算法所产生的序列存在着自相似性,并同时对RMD算法和Fourier变换法的优缺点进行了分析。
Network traffic models are important basis of network programming and performance evaluation. The conventional models are mostly based on Poisson model and Markovian franc model,which is only Short-Range Dependence. With the continuous development of network services, studies found that the actual network traffic has a long-range dependence （LRD） now and in a very long time , which is a kind of self-similarity. In this paper, the RMD and Fourier algorithm were adopted to simulate and analyze FBM model, a self-similar model. They generated the necessary sequence of self-similar traffic. Then the article uses R/S method and variance-time method to verify Hurst value of the generated sequence of self-similar traffic in order to verify the self-similarity of the self-similar traffic sequence. The existence of self-similarity is verified by experiments, and the advantage and disadvantage of RMD and Fourier algorithm are analyzed.
出处
《电子设计工程》
2011年第17期101-104,共4页
Electronic Design Engineering
基金
国家重点实验室基金(9140C2305041001)
