Online Energy-efficient Resource Allocation in Cloud Computing Data Centers
Bibliographic record
Abstract
Energy efficiency is a major topic in every scientific field, since being energy efficient means producing more for a smaller cost. Data centers are no exception to this rule as their energy use represents a large portion of the global consumption, and it is needless to say that they ought to perform optimally while being eco-friendly in order to preserve natural resources as much as possible while providing a high quality service for the users. In this paper, we propose an efficient algorithm for allocating users to a pool of servers in an energy-efficient way. Our allocation model emphasizes the critical importance of nondominant resource types such as memory, which usually tend to be wasted by homogeneous allocation approaches. We show that the performance of the algorithm makes it worthy of being used in real-time environments where split-second decisions must be made. We compare our algorithm to the most well-known metaheuristics used in operations research and we show that they do not provide a significant improvement in a reasonable time.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| 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".