Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements

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.