Proactive & Fine-Grained Monitoring For Microservice Call Chains In Cloud-Native Applications Through Latency Distribution Prediction
Bibliographic record
Abstract
Modern cloud-native applications are distributed in nature and have their health monitored through multiple channels. In this study, we propose a singular novel approach that leverages multi-channel monitoring data for fine-grained performance analysis, proactive anomaly prediction, and root-cause analysis in microservices based applications. To this end, we employ Microservice Embeddings, Graph Neural Networks (GNN), and Gated Recurrent Units (GRU) to predict latency distribution, as opposed to a single latency value, for individual calls within a microservice call chain, as well as the distribution of end-to-end latency. Thus, our approach enables deeper insights into system performance and targeted diagnostics for anomalies. We use several benchmark datasets containing anomalies and show that our approach performs consistently across the latency spectrum while outperforming baseline latency prediction approaches by about 6%. Lastly, we show that our approach can be efficiently used to automate the process of trace-based anomaly prediction and perform root-cause analysis.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".