Exploring Approaches to Integrate Performance Prediction and Anomaly Detection in Microservices Systems
Bibliographic record
Abstract
Numerous algorithms have been proposed over the years to predict performance metrics and detect performance anomalies in microservices based applications. However, most models specialize in either performance prediction or anomaly detection. As a result, multiple models are often required to monitor cloud-native applications effectively. Given the distributed nature of modern cloud-native systems, performance and health monitoring is carried out through various channels, and using separate models for each task adds complexity to the monitoring process. Therefore, this paper aims to investigate the use of multimodal data to integrate performance prediction and anomaly detection methods. For this purpose, we utilize simple Graph Neural Networks to predict latency distribution, as opposed to a single latency value for traces generated by a microservices system. The predicted latency distribution is then fed to several Machine Learning models to predict trace based anomalies. Our results show that all models tested can achieve accuracy of more than 96% and with good precision and recall values in a heavily unbalanced Train Ticket dataset.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".