From Load Tests to Live Streams: Graph Embedding-Based Anomaly Detection in Microservice Architectures

Srinidhi Madabhushi; Pranesh Vyas; Swathi Vaidyanathan; Mayur Kurup; Elliott Nash; Yegor Silyutin

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arXiv · 2604.06448

From Load Tests to Live Streams: Graph Embedding-Based Anomaly Detection in Microservice Architectures

Srinidhi Madabhushi·Pranesh Vyas·Swathi Vaidyanathan·Mayur Kurup·Elliott Nash·Yegor Silyutin

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Abstract

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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Srinidhi Madabhushi, Pranesh Vyas, Swathi Vaidyanathan, Mayur Kurup, Elliott Nash, Yegor Silyutin. 2026-04-14. From Load Tests to Live Streams: Graph Embedding-Based Anomaly Detection in Microservice Architectures. https://arxiv.org/abs/2604.06448

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