Dynamic trace-based sampling algorithm for memory usage tracking of enterprise applications
Why this work is in the frame
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Bibliographic record
Abstract
Excessive memory usage in software applications has become a frequent issue. A high degree of parallelism and the monitoring difficulty for the developer can quickly lead to memory shortage, or can increase the duration of garbage collection cycles. There are several solutions introduced to monitor memory usage in software. However they are neither efficient nor scalable. In this paper, we propose a dynamic tracing-based sampling algorithm to collect and analyse run time information and metrics for memory usage. It is implemented as a kernel module which gathers memory usage data from operating system structures only when a predefined condition is set or a threshold is passed. The thresholds and conditions are preset but can be changed dynamically, based on the application behavior. We tested our solutions to monitor several applications and our evaluation results show that the proposed method generates compact trace data and reduces the time needed for the analysis, without loosing precision.
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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 | 0.001 | 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 | 0.000 |
| 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 it