Failure Analysis and Characterization of Scheduling Jobs in Google Cluster Trace
Bibliographic record
Abstract
Most public and private cloud providers have experienced failure in one of their services that may affect numerous applications and websites. Thus, in order to understand the causes of different types of failures and remediate the issue, failure analysis is one of the most critical steps. Failure analysis has been developed based on monitoring the most significant metrics of the system in order to study the behavior and frequency changes in the systems. Then, the monitored data will be stored in log files to be utilized for analysis and prediction tasks. In this paper, we primarily focus on analyzing and interpreting the characteristic behavior of finished/failed jobs in association with physically available resources using a publicly available dataset, Google cluster trace. The primary objective of our work is to enhance the understanding of job failure in cloud computing environments. Our results show a clear correlation between failed jobs and requested resources including memory, CPU, and disk space. Based on our results, we find that many techniques can be applied to increase the reliability and availability of cloud applications, such as developing scheduling algorithms, predicting job failure, limiting task resubmission or changing the priority policies.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".