Network State Prediction with Attention-Based Graph Convolutional Network

With the increase of 5G devices and the continuous expansion of servers in the Internet, network service providers lack low-cost technologies to accurately predict changes in network traffic. Accurate prediction of network traffic changes is of great significance to network operators’ resource management, traffic engineering, capacity planning, and service quality. However, the network state is not always in a state of steady change in the real environment, but in a state of fluctuation, so the deep learning model close to the real situation must be nonlinear. Network State Prediction with Attention-Based Graph Convolutional Network are used to learn complex topological structures to capture spatial dependencies, and show superiority in handling complex and non-linear graph-structured data. In this paper, we propose an attention- based graph convolutional neural network network state prediction technique. A multihead attention mechanism is introduced in the propagation layer, so that the central node features can be differentiated in the attention of neighboring nodes during the aggregation process. In extensive experiments on real network traffic, it is shown that the proposed network state prediction method outperforms previous methods .