一种稀疏度自适应的网络流量矩阵测量方法

为提高网络流量矩阵测量的精度,在压缩感知框架下提出一种稀疏度自适应的网络流量矩阵测量方法.通过对网络流量矩阵的主成分分析及奇异值归一化处理寻找信号支撑集选择的判定阈值,利用网络流量矩阵重构过程中的残差L2范数匹配计算各测量时间点上网络流量矩阵的稀疏度,减小由于网络流量矩阵近似稀疏表示以及稀疏度选择不准确造成的测量误差.仿真实验结果表明:所提出的方法与现有方法相比能够获得更小的空间相对误差和时间相对误差.通过稀疏度自适应选择方法,能够有效提高网络流量矩阵的测量精度.

A sparsity adaptive measurement algorithm for network traffic matrix

In order to improve the accuracy of the measurement algorithm for traffic matrix, a novel traffic matrix measurement algorithm with compressive sensing is proposed. This algorithm gets the judge gate by the principal components analysis and normalization of singular value. To reduce the measurement error created by approximation of sparse express and inaccurate choice of sparsity, we use L2 formulation of residual error to match the sparsity in the process of reconstitution of the traffic matrix on each time of measurement. Simulation results show that, this algorithm can obtain less spatial relative error and temporal relative error compared with the existing algorithm. With the help of adaptive selection for initial value of sparsity, this algorithm can obtain a higher accuracy.