基于实时分析的网络测量抽样统计模型  

Network Data Measurement and Statistics Model Based on Real-Time Analysis

作　　者：陈松[1] 王珊[2] 周明天[1] 

机构地区：[1]电子科技大学计算机学院,四川成都610054 [2]电子科技大学物理电子学院,四川成都610054

出　　处：《电子学报》2010年第5期1177-1180,共4页Acta Electronica Sinica

基　　金：国家"十一五"科技支撑计划(No.2006BAH02A0407)

摘　　要：由于网络通信量具有自相似性,传统的网络测量抽样方法如均匀抽样、泊松抽样容易与真实数据产生比较大的误差.本文提出了一种基于实时分析的网络抽样统计模型(NMSM),在数据采集过程中,实时分析样本的变化特征,动态调整采集频率,最好程度的完成数据抽样采集.实验证明,NMSM模型在网络变化比较平稳时,对网络的影响程度最低,在网络变化比较大时,对真实数据的拟合程度最好.Traditional network measurement methods,such as uniform sampling and poisson sampling,deviate true data set a lot due to the self-similarity of the network traffic. This paper proposes a new network data measurement and statistics model based on real-time analysis （NMSM） which can be used to analyze the real-time changing characters of the data,adjust the sampling frequency dynamically, then fulfill the data sampling to the best advantage. The experiment showed that if the network environment changed smoothly the NMSM model seldom affected network; and if the network environment changed greatly it fitted the true data set very well.

关 键 词：网络测量 均匀抽样 泊松抽样 SNMP 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]