Prediction of Network Traffic Load on High Variability Data Based on Distance Correlation

Accurate network traffic load (TL) prediction is essential in many networking applications. However, the real TLs in practical networks may have high variability and are difficult to be predicted, which may severely affect users' quality of experience (QoE). To address this problem, we first analyze the real-world network traffic dataset to investigate real TLs properties and find out the distance-correlation between regions in a spatial graph have the potential to improve the prediction result. Hence, we propose a time-series model based method to consider the distance-correlation in an efficient way. Empirically, experimental studies on real data demonstrate that our proposed method can effectively reduce at least 10% error value on regions with high-variability TLs. Finally, we further discuss the impact of the distance-correlation on the TL prediction.