Gated Recurrent Unit-Based Network Traffic Prediction for Load Balance

To improve the reasonable allocation of network resources, and reduce service interruption or overwhelming delay for software-defined networking (SDN) due to excessive network load, this paper proposes a network load balancing scheme based on gated recurrent units (GRU) aiming at optimizing load balancing strategies. First, a link prediction and priority adjustment model is constructed to describe the network load balance optimization problem. Second, a GRU-based network traffic prediction algorithm is proposed to capture network traffic features. Specifically, a graph convolution network (GCN) is utilized to learn the spatial features in the network topology, and GRU is used to capture the long-term dependencies in the traffic data sequences. Finally, a logical ordering and threshold filtering strategy is designed to achieve priority ordering of links and determine the preferred links for services to improve network load balancing. The experimental results show that the GRUbased network traffic prediction algorithm exhibits 3% higher prediction accuracy compared to the existing baseline methods, and the network load balancing scheme based on GRU performs 4 x network load balancing.