Traffic generation methods based on generative adversarial neural networks
Traffic generation in network simulation is crucial for ensuring simulation effectiveness.Currently,common network traffic generators are typically based on a certain random model,where the generated traffic adheres to a specified random distribu-tion.However,determining a realistic random model for actual network traffic is often challenging,leading to biases in current models when simulating real network traffic.To address these issues,this paper proposes a spatiotemporal-correlated traffic gener-ation model based on Generative Adversarial Neural Networks(GANs).The encoding method for network traffic data is im-proved,and Z-score is applied to process traffic data,making the data tend toward a standard normal distribution.Additionally,a measurement method for evaluating the spatiotemporal correlation of network traffic is introduced.Experimental results indicate that,compared to existing baseline generation methods,the proposed approach averages 9％improvement in measures of authen-ticity and correlation.
万方数据
维普
