Integrating Preemption Threshold to Fixed Priority DVS Scheduling Algorithms
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Bibliographic record
Abstract
Dynamic voltage scaling (DVS) is an effective technique to reduce the energy consumption of CMOS powered embedded systems through software control. However, applying fixed priority DVS algorithms introduces increased number of preemptions, which, in turn results in extra time delay and energy cost. Effectively reducing the number of preemptions is therefore required. In this paper, we propose to integrate preemption threshold to fixed priority DVS scheduling algorithms to reduce such negative impact. Two preemption-aware algorithms ccFPPT and FPPT-WDA are studied. Performance evaluations in terms of both energy consumption and the number of preemptions are conducted among different fixed priority DVS algorithms, with or without preemption threshold. The experimental results show that our algorithms with preemption threshold can save up to 60\% number of preemptions and 20\% energy consumption over existing DVS algorithms.
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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