论文标题
缺少Internet流量数据的可行方法
A Parallelizable Method for Missing Internet Traffic Tensor Data
论文作者
论文摘要
从不完整的观察数据中恢复Internet网络流量数据是Internet网络工程和管理中的一个重要问题。在本文中，通过将互联网流量数据中的时间稳定性和周期性功能完全结合，提出了一种新的可分离优化模型，用于Internet数据恢复，该模型基于T产品和张力器的快速离散傅立叶变换。此外，通过使用广义逆矩阵，提出了一种易于操作和有效的算法。从理论上讲，我们证明在适当的条件下，所提出的算法产生的序列的每个积累点都是已建立模型的固定点。在广泛使用的实际互联网网络数据集上进行的数值仿真结果，显示了该方法的良好性能。在中等抽样率的情况下，提出的方法效果很好，其效果比文献中一些现有的互联网流量数据恢复方法的效果更好。优化模型中显示的可分离结构特征为设计更有效的并行算法提供了可能性。
Recovery of internet network traffic data from incomplete observed data is an important issue in internet network engineering and management. In this paper, by fully combining the temporal stability and periodicity features in internet traffic data, a new separable optimization model for internet data recovery is proposed, which is based upon the t-product and the rapid discrete Fourier transform of tensors. Moreover, by using generalized inverse matrices, an easy-to-operate and effective algorithm is proposed. In theory, we prove that under suitable conditions, every accumulation point of the sequence generated by the proposed algorithm is a stationary point of the established model. Numerical simulation results carried on the widely used real-world internet network datasets, show good performance of the proposed method. In the case of moderate sampling rates, the proposed method works very well, its effect is better than that of some existing internet traffic data recovery methods in the literature. The separable structural features presented in the optimization model provide the possibility to design more efficient parallel algorithms.
