An Energy-Efficient Periodic Resource Model for Bounded Delay-Tolerant Real-Time Systems
Bibliographic record
Abstract
In the past few years, the energy consumption of real-time systems has become an essential topic of research, especially with the rise of the internet of things. In hard real-time systems, sensitivity to timing is the focal feature of system behaviors. Real-time systems must consider time constraints (mainly deadlines) to deliver the proper results. Therefore, typical real-time systems are inefficient in resource reservation, where resource supply is always higher than the workload demand and thus consumes more power than required. In firm and soft real-time systems, some deadline violations can be tolerated in a specific time interval. The worst-case execution time can be delayed to abound range. Therefore, instead of over-provisioning resources when considering the demand cannot be higher than supply, we propose a new algorithm that can efficiently estimate the resource reservation and reduce power consumption. The proposed algorithm is theoretically studied, and results show that while still satisfying the delay requirements of control systems, significant energy savings (up to 78.5%) could be achieved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".