A Self-Adaptive Auto-Scaling System in Infrastructure-as-a-Service Layer of Cloud Computing
Bibliographic record
Abstract
The National Institute of Standards and Technology (NIST) defines scalability, resource pooling and broad network access as the main characteristics of cloud computing which provides highly available, reliable, and elastic services to cloud clients. In addition, cloud clients can lease compute and storage resources on a pay-as-you-go basis. The elastic nature of cloud resources together with the cloud's pay-as-you-go pricing model allow the cloud clients to merely pay for the resources they actually use. Although cloud's elasticity and its pricing model are beneficiary in terms of cost, the obligation of maintaining Service Level Agreements (SLAs) with the end users necessitates the cloud clients to deal with a cost/performance trade-off. Auto-scaling systems are developed to balance the trade-off between the cost and the performance by automatically provisioning compute and storage resources for the cloud services.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".