A sequence-to-sequence traffic predictor on software-defined networking

Abstract

Network traffic prediction is very important for load balancing and network planning. This paper proposes an attention-based traffic predictor (ATP) model to achieve traffic prediction in a software-defined network (SDN) environment. To improve the accuracy and efficiency of a prediction, improvements are made from three aspects: data, model and evaluation optimisation. First, a combination of lower sampling frequency and data augmentation is adopted to reduce the resource consumption of the request. Second, based on the long correlation and self-similar characteristics of network traffic, a sequence-to-sequence model with attention (Seq2Seq+Attention) is selected for network traffic prediction. Finally, this paper proposes an improved weighted MSE evaluation method, which is more suitable for network traffic prediction. Experiments show that the proposed method can maintain the prediction accuracy while reducing the sampling frequency by 50%. The weighted MSE evaluation method can improve the accuracy by 5.37% compared with the original MSE evaluation method.