AltOOM: A Data-driven Out of Memory Root Cause Identification Strategy
Bibliographic record
Abstract
Resource-constrained devices face significant performance challenges when encountering memory pressure situations due to limited hardware resources. Existing approaches mainly focus on reactive and instantaneous approaches, but they often fail to accurately identify the root cause of memory pressure, resulting in delayed and ineffective response strategies. In this paper, we address this limitation by proposing an alternative data-driven approach to proactively detect memory pressure and identify the responsible process in resource-limited devices. Our method enables the activation and deactivation of extended process-level profiling based on the predicted memory pressure, facilitating the identification of the root cause process. Through evaluation, we achieved an 85% accuracy in forecasting memory pressure situations and correctly identified the responsible process in 83% of use-cases. These results demonstrate the effectiveness of our strategy to stream large amount of trace data in mitigating memory pressure issues in resource-constrained systems. This approach has the potential to enhance system performance and improve overall system architecture in such devices.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".