Measuring Prediction Sensitivity of a Cloud Auto-scaling System
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
Elasticity is one of the key benefits of cloud computing which helps customers reduce the cost. Although elasticity is beneficiary in terms of cost, obligation of maintaining Service Level Agreements leads to necessity in dealing with the cost-performance trade-off. Proactive auto-scaling is an efficient approach to overcome this problem. In this approach scaling actions are generated based on prediction results. Recently, several research studies have been focusing on improving prediction accuracy in order to improve the efficiency of auto-scaling mechanisms. However, the sensitivity of auto-scaling mechanisms to the prediction results is neglected in the domain. In this work we have investigated the sensitivity of auto-scaling mechanisms to the prediction results by evaluating the influence of performance predictions accuracy on the auto-scaling actions. Specifically, we have compared actions of threshold based scaling techniques which are generated based on Support Vector Machine (SVM) and Neural Networks (NN) predictions. Our experimental results show that although SVM is more accurate than NN, scaling decisions made by the two algorithms are identical in 91.5% of the time. Furthermore, we have shown that the optimal training duration for SVM and NN is about 60% of experiment duration.
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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 | 0.002 | 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