Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements
Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements
复制标题
DOI：
10.1109/tnet.2018.2819504
复制
发表时间：
2018-04
期刊：
影响因子：
--
通讯作者：
Kun Xie;Can Peng;Xin Wang;Gaogang Xie;Jigang Wen;Jiannong Cao;Dafang Zhang;Zheng Qin
中科院分区：
文献类型：
--
作者：
Kun Xie;Can Peng;Xin Wang;Gaogang Xie;Jigang Wen;Jiannong Cao;Dafang Zhang;Zheng Qin
从部分测量数据推断网络流量矩阵对于各种网络工程任务（例如容量规划、负载平衡、路径设置、网络供应、异常检测和故障恢复）变得越来越重要。最近的研究表明，与基于 2-D 矩阵的插值方法相比，3-D 张量有望更准确地对缺失数据进行插值。尽管有潜力，但很难在实际网络中以不同速率进行测量来形成张量。为了解决这些问题，我们提出了一种 Reshape-Align 方案，用来自可变速率测量的数据形成正则张量，并引入用户域和时域因子矩阵，充分利用两个域的特征，将矩阵补全问题转换为基于 CANDECOMP/PARAFAC 分解的张量补全问题，以实现更准确的丢失数据恢复。我们的性能结果表明，我们的 Reshape-Align 方案可以在以下几个指标方面实现显着更好的性能：误差率、平均绝对误差和均方根误差。
The inference of the network traffic matrix from partial measurement data becomes increasingly critical for various network engineering tasks, such as capacity planning, load balancing, path setup, network provisioning, anomaly detection, and failure recovery. The recent study shows it is promising to more accurately interpolate the missing data with a 3-D tensor as compared with the interpolation methods based on a 2-D matrix. Despite the potential, it is difficult to form a tensor with measurements taken at varying rate in a practical network. To address the issues, we propose a Reshape-Align scheme to form the regular tensor with data from variable rate measurements, and introduce user-domain and temporal-domain factor matrices which take full advantage of features from both domains to translate the matrix completion problem to the tensor completion problem based on CANDECOMP/PARAFAC decomposition for more accurate missing data recovery. Our performance results demonstrate that our Reshape-Align scheme can achieve significantly better performance in terms of several metrics: error ratio, mean absolute error, and root mean square error.