Identifying performance deviations in thread pools
Bibliographic record
Abstract
Large-scale software systems handle increasingly larger workloads by implementing highly concurrent and distributed design patterns. The thread pool pattern uses pools of pre-existing and reusable threads to limit thread lifecycle over-head (thread creation and destruction) and resource thrashing (thread proliferation). However, these advantages are weighed against performance issues caused by concurrency risks, like synchronization errors or deadlock, and thread pool-specific risks, like poorly tuned pool size or thread leakage. Detecting these performance issues during load testing requires a thorough understanding of how thread pools behave, yet most performance analysts have limited knowledge of the system and are flooded with terabytes of data from load tests. We propose a methodology to identify threads with performance deviations in thread pools. Our methodology ranks threads based on the dissimilarity of their resource usage metrics. A case study on a large-scale industrial software system shows that our methodology can identify threads with performance deviations with an average precision of 100% and an average recall of 76.61%. Our methodology performs very well when ranking long-lived deviations, such as memory leaks, but more work is needed to rank short-lived deviations, such as CPU spikes.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".