Renewable Energy-Based Multi-Indexed Job Classification and Container Management Scheme for Sustainability of Cloud Data Centers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cloud computing has emerged as one of the most popular technologies of the modern era for providing on-demand services to the end users. Most of the computing tasks in cloud data centers are performed by geodistributed data centers which may consume a hefty amount of energy for their operations. However, the usage of renewable energy resources with appropriate server selection and consolidation can mitigate the energy related issues in cloud environment. Hence, in this paper, we propose a renewable energy-aware multi-indexed job classification and scheduling scheme using container as-a-service for data centers sustainability. In the proposed scheme, incoming workloads from different devices are transferred to the data center which has sufficient amount of renewable energy available with it. For this purpose, a renewable energy-based host selection and container consolidation scheme is also designed. The proposed scheme has been evaluated using Google workload traces. The results obtained prove 15%, 28%, and 10.55% higher energy savings in comparison to the existing schemes of its category.
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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 | 0.000 |
| 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 it