摘要： 针对目前多数网络流量矩阵估计方法都采用单一的SNMP链路负载或抽样的NetFlow数据的问题，提出一种综合使用多个数据源进行流量矩阵估计的方法，将SNMP链路负载与抽样的NetFlow数据结合作为相互纠错码，设计过滤这2种数据源中脏数据的算法。以校园网为实验环境，与通用重力模型方法的比较结果表明，利用该算法去除脏数据后进行流量矩阵估计具有更高的准确性。

关键词: 流量矩阵, 源-目的流量, 卡尔曼过滤

Abstract: Aiming at the problem that most of the previous approaches infer traffic matrix from either SNMP link loads or sampled NetFlow records. This paper designs a novel inference techniques that, by statistically correlating SNMP link loads and sampled NetFlow records. And it designs techniques that, by comparing notes between the above two information sources, identify and remove dirty data, and therefore allow for accurate estimation of the traffic matrices with the cleaned data. Experimental results prove that the algorithm has a higher accuracy.

Key words: traffic matrix, origin-destination traffic, Kalman filtering

中图分类号: 

• TP393