基于ACD模型的网络数据流时域微观特性分析  

Time domain analysis of micro-characters in network flows using ACD model

作　　者：徐正国 郑辉 邓月华 XU Zhengguo;ZHENG Hui;DENG Yueh(National Key Laboratory of Science and Technology on Blind Signals Processing, Chengdu 610041, China)

机构地区：[1]盲信号处理国家科技重点实验室,成都610041

出　　处：《计算机工程与应用》2016年第17期11-15,共5页Computer Engineering and Applications

摘　　要：对网络中不同类型的数据流,应用自回归条件持续期模型(ACD),分析其中存在的时域微观特性,并研究ACD模型对网络数据流时序建模的适用性。使用ACD模型为具有随机到达过程的网络数据流时间序列建模,其优点是能够在不损失原始非等间隔时间序列特性的条件下,直接分析得到数据流的时域微观性质。在对实验数据集统计特性进行研究的基础上,得出数据包到达过程适用ACD模型的基本依据,采用ACD(2,1)模型对不同类型的网络数据流时间序列进行建模,结果表明其具有较好的拟合程度。For distinguishing different kinds of data streams in the network, Autoregressive Conditional Duration(ACD)model is used to analyze the micro-characters of network flows in the time-domain, and the feasibility of the model is testified.One important advantage of ACD model is, without binning the unevenly sampled time series into evenly-spaced, it could obtain the features of network flows in the time-domain directly. Based on the statistical analysis of experimental data sets, the packets’arriving time series in network flows can be fitted in with ACD model, furthermore ACD(2, 1)shows a good performance in modeling network flows of different applications.

关 键 词：自回归条件持续期 网络数据流 非等间隔采样 时间序列 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]