流量矩阵估算算法研究  

Traffic Matrix Estimation Algorithm Based on Square Root Filtering

作　　者：杨扬[1] 周静静[1,2] 杨家海[2,3] 赵巍[1] 熊曾刚[1] 

机构地区：[1]北京科技大学信息工程学院,北京100083 [2]清华大学信息网络工程研究中心,北京100084 [3]清华信息科学与技术国家实验室(筹),北京100084

出　　处：《计算机科学》2009年第7期42-45,共4页Computer Science

基　　金：863国家重点基金项目(2007AA01Z234);国家自然科学基金(90412012;60673160;60873193;60873192)资助

摘　　要：流量矩阵是许多网络规划和流量工程任务的关键输入,但直接监控非常具有挑战性。因此,如何根据有限的先验信息,通过合理建模来估算流量矩阵,成为重要的研究课题。已有的估算方法中,卡尔曼方法是一个相对高效和精确的方法,然而,它在实际网络环境中使用时存在"坏态"现象,导致数值计算困难。提出了平方根滤波/平滑流量矩阵估算算法对卡尔曼方法进行改进;并针对新算法的需要,提出了流量数据预处理的方法,可滤除有大量噪声的"坏"数据。模拟仿真结果显示新算法的精确性和稳定性都优于卡尔曼滤波方法。The traffic matrix is one of the crucial inputs in many network planning and traffic engineering tasks, but it is usually impossible to directly measure traffic matrices. So, it is an important research topic to infer traffic matrix by reasonably modeling, and incorporating the limited empirical information. Of the proposed methods, Kalman Filtering method is a more efficient and accurate method than many others. However, the error covariance calculation components of the Kalman Filtering are difficult to implement in realistic systems due to the existence of ill-conditioning problems. The authors proposed Square Root Filtering/Smoothing traffic matrix estimation（SRFsTME） algorithm to improve it, and also proposed a data pre-filtering method to reject the ＂bad＂ data with considerable noise. Simulation and actual traffic testing results show that SRFsTME algorithm is more numerical accurate and stable than Kalman Filtering.

关 键 词：流量矩阵 源-目的流量 卡尔曼滤波 平方根分解 

分 类 号：TP393.07[自动化与计算机技术—计算机应用技术]