一种缺失网络流量张量数据的可量化方法
A Parallelizable Method for Missing Internet Traffic Tensor Data
摘要：
从不完整的观测数据中恢复网络流量数据是网络工程和管理中的一个重要问题。本文充分结合互联网流量数据的时间稳定性和周期性特征，提出了一种基于t积和张量快速离散傅里叶变换的互联网数据恢复可分离最优化模型。利用广义逆矩阵，提出了一种简单有效的算法。在理论上，我们证明了在适当的条件下，该算法产生的序列的每个聚点是所建立的模型的一个平稳点。在广泛使用的真实互联网网络数据集上进行的数值模拟结果表明，所提出的方法具有良好的性能。在中等采样率的情况下，该方法效果良好，其效果优于文献中现有的一些互联网流量数据恢复方法。该最优化模型所提出的可分离结构特征为设计更高效的并行算法提供了可能。
英文摘要：
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.
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