Generative spatiotemporal image exploitation for datacenter traffic prediction

Abstract

The tremendous growth rate of global internet traffic in past years increases the importance of traffic prediction for network operators to ensure seamless Quality of Service (QoS) with proactive traffic engineering. Even minor anomalies in traffic management can lead to service disruptions that affect a vast user base, necessitating highly accurate traffic predictions. While recent studies have exploited Deep Learning for accurate traffic predictions, most of them have targeted mobile network traffic and they often fall short in delivering precise long-range predictions and effective spatiotemporal feature extraction from single-stream time-series data. This research addresses these limitations by proposing a Convolutional Recurrent Generative Adversarial Network (CoRe-GAN) consisting of generator and discriminator neural networks for high-accuracy traffic prediction. The generator with Convolutional Long Short-Term Memory (ConvLSTM) model effectively captures intricate features, whereas the discriminator utilizes a Convolutional Neural Network (CNN) to train the generator through feedback. Moreover, advanced training techniques like fact forcing and feature matching increase the learning convergence rate, avoid mode collapse, and amplify prediction accuracy of CoRe-GAN. The evaluation with Pangyo Network Dataset (PND) and synthetic Intrusion Detection Dataset (IDD) confirms CoRe-GAN superiority. The results show that it outperforms ConvLSTM models with an average 20% and 16% lower Mean Square Error (MSE) with PND and IDD traffic data, respectively.