Adaptive management of energy consumption using adaptive runtime models
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
A promising avenue to control energy-related costs in enterprise data centers is to investigate power-aware resource management strategies. In this study we investigate techniques to schedule resources adaptively with the sole aim of reducing power consumption. Our approach is based on a characterization of energy usage and resource utilization patterns obtained by monitoring energy consumption in an enterprise data center. We propose an adaptive feature extraction method to classify resource utilization patterns from energy consumption data. Improved classification results are obtained through signal feature extraction prior to the training stages for cascading classifiers for at least 14 different energy usage patterns. Adaptive feature extraction prior to classifier training improved class identification even further. The identified patterns can now be used as a basis for adaptive resource scheduling within a power-smart data center. The classification method that performed best is part of our proposed energy runtime model and controller which manages and controls the energy consumption in the data center according to usage patterns.
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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 | 0.000 | 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