用图嵌入识别微服务中负载测试未覆盖的异常服务
From Load Tests to Live Streams: Graph Embedding-Based Anomaly Detection in Microservice Architectures
- 基于GCN-GAE构建服务图嵌入，捕捉分钟级依赖关系
- 通过余弦相似度比对测试与真实流量，发现96%高精度异常
- 适合大规模微服务系统运维人员，可提前预警关键故障
Prime Video定期进行负载测试以模拟周四晚橄榄球等直播活动及《权力之环》等点播事件的观众流量高峰。尽管这些压力测试验证了系统容量，但有时会遗漏真实事件流量下的独特服务行为。我们提出一种基于图的异常检测系统，利用无监督节点级图嵌入识别被低估的服务。该系统基于GCN-GAE，从分钟级分辨率的有向加权服务图中学习结构表示，并通过负载测试与事件嵌入间的余弦相似度进行异常标记。系统成功识别出已记录的事故相关服务，展现出早期检测能力。我们还引入一个初步的合成异常注入框架用于可控评估，结果显示精度达96%，误报率仅0.08%，但受保守传播假设影响，召回率仅为58%。该框架在Prime Video中体现实际价值，同时揭示方法论启示与改进方向，为微服务生态的广泛部署奠定基础。
原文摘要 · Abstract (English)
Prime Video regularly conducts load tests to simulate the viewer traffic spikes seen during live events such as Thursday Night Football as well as video-on-demand (VOD) events such as Rings of Power. While these stress tests validate system capacity, they can sometimes miss service behaviors unique to real event traffic. We present a graph-based anomaly detection system that identifies under-represented services using unsupervised node-level graph embeddings. Built on a GCN-GAE, our approach learns structural representations from directed, weighted service graphs at minute-level resolution and flags anomalies based on cosine similarity between load test and event embeddings. The system identifies incident-related services that are documented and demonstrates early detection capability. We also introduce a preliminary synthetic anomaly injection framework for controlled evaluation that show promising precision (96%) and low false positive rate (0.08%), though recall (58%) remains limited under conservative propagation assumptions. This framework demonstrates practical utility within Prime Video while also surfacing methodological lessons and directions, providing a foundation for broader application across microservice ecosystems.
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