Online characterization of buggy applications running on the cloud
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
As Cloud platforms are becoming more popular, efficient resource management in these Cloud platforms helps the Cloud provider to deliver better quality of service to its customers. In this paper, we present an online characterization method that can identify potentially failing jobs in a Cloud platform by analyzing the jobs' resource usage profile as the job runs. We show that, by tracking the online resource consumption, we can develop a model through which we can predict whether or not a job will have an abnormal termination. We further show, using both real world and synthetic data, that our online tool can raise alarms as early as within the first 1/8th of the potentially failing job's lifetime, with a false negative rate as low as 4%. These alarms can become useful in implementing either one of the following resource-conserving Cloud management techniques: alerting clients early, de-prioritizing jobs that are likely to fail or assigning them less performant resources, deploying or up-regulating diagnostic tools for potentially faulty jobs.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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