LLMs achieve competitive precision and recall versus MARS on several of 16 microservice anti-patterns when evidence is local or semantically rich, but underperform on structural and cross-service cases.
Tracegra: A trace-based anomaly detection for microservice using graph deep learning.Computer Communications, 204:109–117, 2023
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A two-layer GCN on 21,438 request-level invocation graphs from a Docker-based microservice benchmark reaches 96.2% accuracy under random split but is outperformed by non-graph baselines under stricter trial-level splits.
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Are LLMs Ready for Anti-Pattern Detection in Microservice Architectures?
LLMs achieve competitive precision and recall versus MARS on several of 16 microservice anti-patterns when evidence is local or semantically rich, but underperform on structural and cross-service cases.
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Can Graph-Based Microservice Performance Detection Be Used for Microservice Intrusion Detection?
A two-layer GCN on 21,438 request-level invocation graphs from a Docker-based microservice benchmark reaches 96.2% accuracy under random split but is outperformed by non-graph baselines under stricter trial-level splits.