An iterated local search approach for carbon footprint optimisation in an intercloud environment
Bibliographic record
Abstract
In this paper, we address the problem of virtual machine (VM) placement in an InterCloud with regard to the reduction of the environmental impact of such environment. We propose a mathematical formulation based on a smart workload consolidation method and a cooling maximisation technique that considers the dynamic behaviour of the cooling fans. As the virtual machine placement problem (VMPP) is classified as an NP-hard problem, we propose an implementation of the iterated local search (ILS) algorithm, ILS_CBF, in order to find good solutions in a reasonable time. Computational results allow to identify the parameters that reduce the carbon footprint costs. The comparison of the proposed heuristic with the exact method and other algorithms demonstrate that the obtained costs are relatively close to the lower bounds, ranging from 0% to a maximum distance less than 2.6%, and allow a good tradeoff between the quality of the solution and the computational 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 | 0.002 | 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 | 0.001 | 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".