Efficient Detection of Communication-related Performance Anti-patterns in Microservices

Modern microservice-based applications are inherently complex due to their distributed nature and intricate service interactions, making performance diagnosis challenging. A significant source of degradation in such systems is Software Performance Anti-patterns, which can arise from inefficient coding practices, poor architectural design, or suboptimal deployment strategies. Existing Anti-pattern detection methods often rely on costly, intrusive data collection, limiting their practicality in production environments. We propose a low-cost, non-intrusive approach for detecting communication-related Anti-patterns by combining selectively captured networking-related system call traces with distributed observability traces. This hybrid tracing enables detailed correlation between low-level communication metrics and high-level service interactions, supporting accurate detection of Anti-patterns such as Blob and Empty-semi-trucks. To minimize overhead, only essential system call events are collected, while distributed tracing data is used for root cause analysis at the service and operation level. We design a machine learning pipeline, supporting super- vised, semi-supervised, and unsupervised detection, achiev- ing up to 91% accuracy with just 2.74% data collection over- head. Offline model training further supports seamless in- tegration into Continuous Integration/Continuous Deploy- ment (CI/CD) workflows for early performance regression detection. Our approach is validated on the DeathStarBench microservice benchmark across 14 scenarios under both clean and noisy conditions. Results show high detection accuracy, effective root cause identification (over 80% match to manual analysis), and minimal runtime impact. This work offers a practical, scalable, and accurate solution for SPA detection in complex microservice environments.