Identifying performance deviations in thread pools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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