Resource requests prediction in the cloud computing environment with a deep belief network
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
Summary Accurate resource requests prediction is essential to achieve optimal job scheduling and load balancing for cloud Computing. Existing prediction approaches fall short in providing satisfactory accuracy because of high variances of cloud metrics. We propose a deep belief network (DBN)‐based approach to predict cloud resource requests. We design a set of experiments to find the most influential factors for prediction accuracy and the best DBN parameter set to achieve optimal performance. The innovative points of the proposed approach is that it introduces analysis of variance and orthogonal experimental design techniques into the parameter learning of DBN. The proposed approach achieves high accuracy with mean square error of [10 −6 ,10 −5 ], approximately 72 % reduction compared with the traditional autoregressive integrated moving average predictor, and has better prediction accuracy compared with the state‐of‐art fractal modeling approach. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 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