This paper focuses on intelligent routing in microservice systems and proposes an end-to-end optimization framework based on graph neural networks. The goal is to improve routing decision efficiency and overall system performance under complex topologies. The method models invocation relationships among microservices as a graph. In this graph, service nodes and communication links are treated as graph nodes and edges. Multi-dimensional features such as node states, link latency, and call frequency are used as input. A multi-layer graph neural network is employed to perform high-order information aggregation and structural modeling. The model outputs a score for each candidate service path. These scores are then used to guide dynamic routing decisions. To improve the model's ability to assess path quality, an edge-aware attention mechanism is introduced. This mechanism helps the model capture instability and bottleneck risks in service communications more accurately. The paper also conducts a systematic analysis of the model's performance under different network depths, topology densities, and service scales. It evaluates the effectiveness of the method in terms of routing accuracy, prediction error, and system stability. Experimental results show that the proposed method outperforms existing mainstream strategies across multiple key metrics. It handles highly dynamic and concurrent microservice environments effectively and demonstrates strong performance, robustness, and structural generalization.

翻译：本文聚焦于微服务系统中的智能路由问题，提出了一种基于图神经网络的端到端优化框架，旨在提升复杂拓扑下路由决策效率与系统整体性能。该方法将微服务间的调用关系建模为图结构，其中服务节点与通信链路分别作为图的节点与边，并综合节点状态、链路延迟、调用频率等多维特征作为输入。通过多层图神经网络进行高阶信息聚合与结构建模，模型输出各候选服务路径的评分，进而指导动态路由决策。为提升模型对路径质量的评估能力，引入了边感知注意力机制，使模型能更精准地捕捉服务通信中的不稳定因素与瓶颈风险。本文系统分析了模型在不同网络深度、拓扑密度及服务规模下的性能表现，从路由准确率、预测误差及系统稳定性等方面评估了方法的有效性。实验结果表明，所提方法在多项关键指标上优于现有主流策略，能够有效应对高动态、高并发的微服务环境，展现出优异的性能、鲁棒性及结构泛化能力。