Towards Efficient Diagnosis of Performance Bottlenecks in Microservice-Based Applications (Work In Progress paper)
Bibliographic record
Abstract
Microservices have been a cornerstone for building scalable, flexible, and robust applications, thereby enabling service providers to enhance their systems' resilience and fault tolerance. However, adopting this architecture has often led to many challenges, particularly when pinpointing performance bottlenecks and diagnosing their underlying causes. Various tools have been developed to bridge this gap and facilitate comprehensive observability in microservice ecosystems. While these tools are effective at detecting latency-related anomalies, they often fall short of isolating the root causes of these problems. In this paper, we present a novel method for identifying and analyzing performance anomalies in microservice-based applications by leveraging cross-layer tracing techniques. Our method uniquely integrates system resource metrics-such as CPU, disk, and network consumption-with each user request, providing a multi-dimensional view for diagnosing performance issues. Through the use of sequential pattern mining, this method effectively isolates aberrant execution behaviors and helps identify their root causes. Our experimental evaluations demonstrate its efficiency in diagnosing a wide range of performance anomalies.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".