Distributed neural tensor completion for network monitoring data recovery

Abstract

Network monitoring data is usually incomplete, accurate and fast recovery of missing data is of great significance for practical applications. The tensor-based nonlinear methods have attracted recent attentions with their capability of capturing complex interactions among data for more accurate recovery. However, the training process of existing methods is often time-consuming due to massive data and unreasonable network resource allocation. Thus motivated, we propose a distributed neural tensor completion method, named D-NORM, which simultaneously optimizes both recovery accuracy and time. Specifically, D-NORM adopts two schemes to solve the resulting optimization problem. First, we design a parameter-efficient multi-layer architecture with convolutional neural network to learn nonlinear correlations among data. Second, we reformulate the initial model as an equivalent set function optimization problem under a matroid base constraint. After constructing an approximate supermodular function to substitute the objective set function with provable upper bound, we propose an approximation algorithm based on the two-stage search procedure with theoretical performance guarantee to rationally allocate computing resources and efficiently recover missing data. Extensive experiments conducted on real-world datasets validate the superiority of D-NORM in both efficiency and effectiveness.