Efficient Detection of Communication-related Performance Anti-patterns in Microservices
Bibliographic record
Abstract
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. Keywords: software performance anti-patterns, tracing, system calls, kernel tracing, distributed tracing, semi-supervised learning
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".