Analyzing Performance Variability in Alibaba's Microservice Architecture: A Critical-Path-Based Perspective
Why this work is in the frame
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Bibliographic record
Abstract
In large-scale microservice architectures, such as those utilized by Alibaba, identifying and addressing performance bottlenecks is a significant challenge due to the complicated interactions between thousands of services. To navigate this challenge, we have developed a critical-path-based technique aimed at analyzing microservice interactions within these complex systems. This technique facilitates the identification of critical nodes where service requests experience the longest delays. Our contribution is the discovery of performance variability in service interactions' response times within these critical paths, and pinpointing specific interactions within the system that show a high degree of performance variability. This improves the ability to detect service performance issues and their root causes allowing for dynamic adjustment in data collection detail, and targets critical interactions for adaptive monitoring.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it