Network Slicing End-to-end Latency Prediction Based on Heterogeneous Graph Neural Network

End-to-end latency,as a crucial performance metric for network slicing,is difficult to predict accurately via modeling due to the influences of network topology,traffic model,and scheduling policies.To tackle the above issues,we propose a heterogeneous graph neural network-based network slicing latency prediction(HGNN) algorithm,where the hierarchical heterogeneous graph of slice-queue-link is constructed to implement the hierarchical feature representation of the slice.Then,considering the attribute characteristics of three types of nodes in the hierarchical graph,i.e.slices,queues,and links,a heterogeneous graph neural network is presented to extract the underlying slice-related features such as topological dynamic changes,edge feature information,and long dependency relationships.Specifically,the graph neural network GraphSAGE,the graph neural network EGRET,and gated recurrent unit GRU are respectively adopted to extract the features of slices,queues,and link.Meanwhile,the iterative update of network slice feature representation and accurate prediction of slice latency are achieved using deep regression based on the heterogeneous graph neural network.Finally,a slice database with various topologies,traffic models,and scheduling policies is constructed using OMNeT++,and the effectiveness of HGNN in predicting slice end-to-end latency is validated on this database.Additionally,by comparing with other graph deep learning-based slice latency prediction methods,the superiority of HGNN in terms of prediction accuracy and generalization is further verified.