用图神经网络预测微服务调用关系，提前发现性能问题。
Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures
- 基于图注意力网络建模微服务调用时序关系。
- 在真实数据上达到高AUC与F1分数，预测准确率优。
- 适合做分布式系统主动监控与资源优化的工程师。
分布式系统中微服务架构的管理复杂且资源消耗大，源于服务间交互频繁且动态变化。准确预测未来交互可增强自适应监控，提前发现并解决潜在性能问题。本文提出一种基于图神经网络（GNN）的方法，特别采用图注意力网络（GAT）进行微服务调用图中的链接预测。与社交网络中稀疏、偶发的交互不同，微服务调用图具有高频、时间敏感的特点，直接影响系统性能。该方法结合时间分段、先进负采样及GAT注意力机制，精准建模复杂交互。基于真实数据集评估，模型在AUC、精确率、召回率和F1分数等指标上表现优异，验证了其在预测微服务交互中的高准确性和鲁棒性。研究结果表明，GNN具备在分布式系统中实现主动监控的潜力，为自适应资源管理与性能优化提供支持。
原文摘要 · Abstract (English)
Managing microservice architectures in distributed systems is complex and resource intensive due to the high frequency and dynamic nature of inter service interactions. Accurate prediction of these future interactions can enhance adaptive monitoring, enabling proactive maintenance and resolution of potential performance issues before they escalate. This study introduces a Graph Neural Network GNN based approach, specifically using a Graph Attention Network GAT, for link prediction in microservice Call Graphs. Unlike social networks, where interactions tend to occur sporadically and are often less frequent, microservice Call Graphs involve highly frequent and time sensitive interactions that are essential to operational performance. Our approach leverages temporal segmentation, advanced negative sampling, and GATs attention mechanisms to model these complex interactions accurately. Using real world data, we evaluate our model across performance metrics such as AUC, Precision, Recall, and F1 Score, demonstrating its high accuracy and robustness in predicting microservice interactions. Our findings support the potential of GNNs for proactive monitoring in distributed systems, paving the way for applications in adaptive resource management and performance optimization.
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