Feedback Scheduling of Real-Time Control Tasks in Power-Aware Embedded Systems
Bibliographic record
Abstract
Power awareness has become a critical issue in real-time scheduling of embedded systems. In the context of control applications, the goal of high control performance and low energy consumption are at odds with each other. While dynamic voltage/frequency scaling (DVS) has proved to be promising in energy saving while preserving task schedulability, traditional DVS algorithms use either open loop or ad hoc solutions, and hence cannot perform well for dynamic systems where the workload varies significantly. By targeting these systems, a novel scheme, namely DVS-FS, which combines DVS and feedback scheduling, is suggested. The objective is to save CPU energy as much as possible, while still providing control performance guarantees, which largely depends on successful schedule of the control task set. DVS-FS exploits feedback control methodology, and facilitates tradeoffs between energy consumption and control performance through controlling the CPU utilization at a considerably high level. Simulation experiments demonstrate that DVS-FS can easily reduce significant energy consumption at the expense of only minor control performance degradation.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".