Reducing Energy Consumption for Reconfiguration in Cloud Data Centers
Bibliographic record
Abstract
Mobile Cloud Computing (MCC) leverages mobile devices and infrastructure equipment to increase services accessibility. It uses increased devices computing capability to enhance services usability and ensure high availability. This growth in performances results in an increased interest for platforms use to accommodate a multitude of applications. To support such an increase in demand, new designs for resource management have to be implemented in order to reach usage optimality. In this work, we propose to design new algorithms to optimize MCC resources management techniques based on stochastic networks optimization. Our approach is focused on energy consumption optimization on the cloud data center side while ensuring resources elasticity to adapt to users' demands and insure a highly available platform. We elected an overclocking technique to enhance servers' capabilities and Lyapunov improvisation to ensure design stability and to minimize the energy cost. We perform extensive simulations under different charge conditions in order to prove the design effectiveness in ensuring the service with lower power consumption. Simulations results confirm the effectiveness of the proposed resources management design.
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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 | 0.000 |
| 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 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".