Order-preserved Tensor Completion For Accurate Network-wide Monitoring

Network-wide monitoring is important for many network functions. However, monitoring data are often incomplete due to the need of sampling to reduce high measurement cost, system failure, and unavoidable transmission loss under severe communication. Instead of only targeting to estimate all missing monitoring data entries with a small set of measurement samples, we study a new order-preserved monitoring data estimation problem to accurately estimate the missing data entries while preserving the data entries' order in the dataset. We propose a novel order-preserved tensor completion model that integrates both the low rank property and the order information into a joint learning problem to estimate the missing data. With well designed non-convex function to directly approximate the tensor rank and order-preserved constraint under the linear self-recovery method, our model can not only more accurately capture the low-rank property of monitoring data to increase the estimation performance of missing data, but also can capture the order information in monitoring data to ensure the estimation accuracy. Extensive experiments using four real datasets demonstrate that compared with the state-of-the-art tensor completion algorithms, our proposed algorithm can provide more accurate estimation and keep the value order of recovered entries to more effectively retrieve top-k large entries.