摘要： 大规模网络中的节点存在高度动态性、不可达性等问题，使得直接测量节点之间时延有时根本无法进行。为此，提出一种非负矩阵坐标分解预测方法，通过获取普通节点与基准节点的RTT值构建一个特征矩阵(非负矩阵)，并在欧式空间坐标计算的基础上，根据距离矩阵的非负性，确保其无损分解成基矢量和权矢量乘积，降低距离计算的维度，加快预测的收敛，其预测精确性符合预测误差趋势分布 模型。

关键词: 距离预测, 非负矩阵, 预测误差

Abstract: As the existence of nodes’ highly dynamic character and nodes’ non-reachability in large-scale network, direct measurement of the node-delay is sometimes simply impossible. This paper proposes a method of Non-negative Matrix Factorization Prediction(NMFP), constructs a characteristic matrix for the RTT between common node and benchmark node. Utilizing method of coordinate calculation in European space, according to the non-negative characteristic of distance matrix, it ensures the matrix non-destructively broken down into the product of the base vector and weight vector. This approach reduces the dimension of distance calculation, speeds up the convergence of prediction, the accuracy of the prediction in line with the trend forecast prediction error distribution model.

Key words: distance prediction, non-negative matrix, prediction error

中图分类号: 

• N945