Task Failure Prediction Using Machine Learning Techniques in the Google Cluster Trace Cloud Computing Environment
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".