Automatic Reduction of Execution Trace Data Volume UsingGradient Boosting in Large-Scale Microservice Systems
Bibliographic record
Abstract
In the complex world of online services that rely on interconnected microservices, ensuring these components have the necessary resources for optimal performance is crucial. Traditionally, analyzing extensive logs and traces from these systems has been the method to model performance, but collecting too little or too much data can present challenges for accurate performance modeling. This highlights the need for a more efficient approach. Our study introduces a streamlined two-phase method that leverages gradient boosting algorithms to pinpoint key data features essential for predicting CPU and memory demands accurately. By focusing on feature importance, we were able to significantly reduce the amount of data required for analysis—by more than 69\%—without compromising, and in some cases enhancing, the accuracy of our models. This evaluation was significantly strengthened by employing a comprehensive dataset provided by Alibaba, illustrating the practical application and validation of our method in a real-world, large-scale microservice environment. Further analysis on our results reveal that most of the identified features for for data volume reduction were mostly focused on the critical aspects of a microservice architecture, notably inter-service communication and resource access patterns. Our findings demonstrate that by concentrating on the most influential features of the microservice architecture trace data, it is possible to maintain, and potentially improve, system performance modeling with substantially less data, presenting a promising research direction for resource optimization in large-scale microservice performance modeling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".