自相似网络业务的一个FARIMA模型

A FARIMA-BASED MODEL FOR THE SELF-SIMILAR NETWORK TRAFFIC

近来发现 ,高速网络业务具有自相似及长相关特性 .分数噪声可描述该类业务 ,但它仅表现长相关特性 .给出了利用 FARIMA(自回归分数整合滑动平均 )模型拟合自相似网络业务的一整套方法 .该模型同时刻画了实际业务的长相关与短相关特性 .通过对实测数据的实验 。

The recent measurements have shown the existence of long range dependence and self similarity in real network traffic. Some researchers use FGN (fractional Gaussian noise) as a traffic model for this sort of traffic, but FGN can only capture the long range dependence. In this paper, a new method is suggested, which uses the so called FARIMA (fractional autoregressive integrated moving average) to modeling network traffic. The method of building the model is given in implementation detail. FARIMA ( p,d,q ) model is a good traffic model that is capable of capturing both the long range and short range behavior of a network traffic. The method is also applied to the real network traffic data. The experiments show that FARIMA model could be used to model actual traffic on quite a large time scale. Compared with other short range dependent processes such as ARMA models, less parameters are required by the FARIMA models. The performance comparisons between FARIMA and other traditional models such as AR, ARIMA and FGN are presented, and the results show that FARIMA model is better than other traditional traffic models. Future work includes applying FARIMA model to various long term and/or short term traffic predictions.