Task Failure Prediction Using Machine Learning Techniques in the Google Cluster Trace Cloud Computing Environment
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
Cloud computing has grown into a critical technology by enabling ground-breaking capabilities for Internet-dependent computer platforms and software applications. As cloud computing systems continue to expand and develop, the need for a more guaranteed, reliant service, and an early task execution status from Cloud Service Providers (CSP) is vital. Additionally, efficient prediction of task failure significantly improves the running time as well as resource utilization in cloud computing. Task failure forecasting in the cloud is regarded as a challenging task based on the literature review conducted in this study. To address these issues, the goal of this study aimed to create fast machine learning approaches for reliably predicting task failure in cloud computing and analyzing their performance using multiple assessment criteria. The Google cluster dataset was used in this study, coupled with Artificial Neural Network (ANN), Support Vector Machine (SVM), and a stacking ensemble method, to forecast job failure in a cloud computing context. The results show that the proposed models can predict the failed tasks both effectively and efficiently. The stacking ensemble outperformed the experimented models, reaching a 99.8%. The suggested paradigm could greatly benefit cloud service providers by decreasing wasted resources and costs associated with task failures.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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