Service Demand Prediction with Incomplete Historical Data

Predicting service demand of users based on the historical network traffic data in a large area has been of great importance towards better service management for geo-distributed systems, such as edge computing. Existing works relying on complete information of historical service demands cannot deal with real-world scenarios that a significant portion of historical data is not available because collecting such data comprehensively is either costly or impossible. Motivated by the challenge of data incompleteness for the demand prediction, in this paper, we propose a deep neural network model based on the encoder-decoder architecture consisting of an encoder, a converter, and a decoder. This model successfully extracts global features from incomplete historical data, predicts the future information in the feature space, and finally generates complete future demands of the whole area from future features. We devise an encoder with mixed 3D and 2D convolutional layers such that rich temporal and spatial features of service demands can still be captured even when historical data are partially missing. We embed the conditional generative adversarial networks (GANs) into our decoder that generates real demand distribution with magnified quality. Another major difference from the traditional encoder-decoder architecture is to establish a fine-grained association between the features of history and future by adding the converter in the middle of encoder and decoder. With the three components, our model can overcome the data incompleteness challenge and use the extracted global features to make an accurate prediction of service demands. We conduct experiments on a real-world dataset with demands of telecommunications services in an urban environment. The experimental results show that our model achieves much higher prediction accuracy than the state-of-the-art approaches when only incomplete historical data are available.