中文摘要：

时延矩阵的重建是延迟敏感型应用优化的重要基础。在深入探讨分布式网络环境下一类基于矩阵分解的非梯度下降重建算法鲁棒性的基础上,分析了时延序列抖动对算法中的不适定与病态问题反演求解的强烈影响。为了降低这种影响,在引入正则化项改善系数矩阵谱特征的基础上,提出了一种时延序列的中值-卡尔曼时空联合滤波框架以抑制抖动污染,并通过统计特征的提取实现了拓扑突变感知,从而提高动态环境下的时延矩阵重建的性能。实验结果表明,滤波重建算法可在保留时延序列主要统计特征的基础上有效避免时延噪声造成的性能损失,并提供平稳的时延估计服务,始终将应力系数保持在较低的水平上。

英文摘要：

Latency matrix completion is an important foundation of latency-sensitive applications optimization. On the basis of the in-depth discussion of the robustness of a kind of matrix-factorization based non-gradient descending completion methods, this paper analyzes the significant impact to the intrinsic ill-posed and ill-conditioned inverse problems in the methods caused by the oscillations of the latency sequences. To mitigate the impact and improve the performance of the matrix completion methods in the wild, a regularization factor is introduced to improve the spectrum signature of the coefficient matrix, a median-Kalman filter, a time-spatial federated filtering scheme, is proposed to smooth the latency sequences, and then the topology mutation is obtained through extracting the statistic characters of the latency sequences. The experiments show that our method can avoid the performance degradation caused by noises without losing the major characteristics of the latency sequences, provide robust latency estimation capability, and keep the stress coefficient at a low level about 0.13 during the whole life cycle of the network.