EAGLE: Heterogeneous GNN-based Network Performance Analysis

Performance analysis is of great importance for management and optimization of space-terrestrial integrated networks (STINs). Traditional approaches to network performance analysis are often based on idealized assumptions that are deviated from the real network environment. This leads to the fact that these models are usually inefficient and restricted in real-world STINs with complicated behavior and even dynamic capacity. In this paper, we propose a network performance analysis approach EAGLE based on heterogeneous graph neural networks. Firstly, we propose a powerful computer network representation model that can preserve all of the information in computer networks. It represents different components of computer networks as a set of heterogeneous nodes and edges, and finally constructs a heterogeneous graph. Then, we obtain the topological representation for the routers in the network through a bandwidth-aware network embedding model. Based on this heterogeneous graph, we propose a heterogeneous GNN model to accurately predict network KPIs because it can completely capture the rich topological and attribute information of computer networks. Experimental results demonstrate that EAGLE can accurately model different networks, and outperforms both traditional methods and the latest neural network-based methods.