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Spatio-temporal tensor completion for imputing missing internet traffic data

Network traffic data consists of Traffic Matrix (TM), which represents the volumes of traffic between Origin and Destination (OD) pairs in the network. It is a key input parameter of network engineering tasks. However, direct measurement of the OD pairs traffic is usually not feasible. Even good traffic measurement systems can suffer from errors, missing data. So obtaining the ODs traffic precisely is a challenge. Existing completion methods often perform poorly for network traffic estimation. Their recovery accuracy tends to be significantly worse when the data loss rate is high. Taking into account network traffic lower-dimensional latent structure and traffic hidden characteristic, a tensor (multi-way array) is introduced to model a time series of pure spatial traffic matrices in this paper. To recover the missing entries in tensors of traffic data, a novel spatio-temporal tensor completion method has been proposed. This approach not only takes advantage of tensor decomposition and its lower-dimensional representation, but also well takes into account traffic spatio-temporal properties. The extensive experiments with the real-world traffic trace data show that the proposed method can significantly reduce the missing traffic data recovery errors and achieve satisfactory completion accuracy comparing with the state-of-the-art completion methods.