Distributed neural tensor completion for network monitoring data recovery


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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.
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Key words
Network data recovery,Tensor completion,Distributed neural tensor completion,Convolutional neural network
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