基于实时分析的网络测量抽样统计模型

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.

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

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 realt-ime 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.