An Enhanced Virtualization of Resources for High Performance Applications in Cloud Computing Using Deep Regression Model
Bibliographic record
Abstract
This paper proposes an Enhanced Virtualization of Resources (EVR) system for high performance applications in Cloud Computing. It uses a Deep Regression Model (DRM) to predict the resource requirements of an application to be deployed on the Cloud. The model takes into account various parameters like number of users, bandwidth requirements, processing time, number of I/O requests and server capability to make accurate predictions. The model is further optimized with a Genetic Algorithm, which uses mutation, crossover and selection operations to ensure the model produces a high-accuracy output. The resulting model is then used by the EVR system to decide which server nodes should be allocated to the application for best performance. The performance of the EVR system is evaluated using metrics such as Cloud query time, Cloud response time, and Cloud resource allocation accuracy. Results demonstrate that the proposed system can provide up to 73.3% more efficiency than existing approaches in Cloud virtualization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".