Energy-aware resource selection on opportunistic grids
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
Energy consumption has been a constant concern for high-performance computing (HPC). Recently, this concern has gained attention from the research community, which is aiming to reduce its costs. The performance gain in such an environment is usually proportional to cost. Examples of such environments are computational grids, which are used in the academic and enterprise domains. On the other hand, one way of obtaining high-performance computing with low-cost investment is by using opportunistic grids, which have become a viable alternative to super-computers and dedicated clusters. This paper proposes an energy-aware resource-selection algorithm to reduce energy consumption in opportunistic grids. The proposed algorithm takes into consideration resource status as well as actions to be taken before allocation to calculate energy consumption. Experimental analysis conducted in this study, taking into account network traffic and node status, shows that a more efficient resource-selection outcome can be obtained, leading to reduced energy consumption. Tests demonstrate an energy-consumption reduction of around 9.5% compared to a commonly used approach.
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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 | 0.000 | 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 it