Scheduling Co-Design for Reliability and Energy in Cyber-Physical Systems
Bibliographic record
Abstract
Energy aware scheduling and reliability are both very critical for real-time cyber-physical system design. However, it has been shown that the transient faults of a system will increase when the processor runs at reduced speed to save energy consumption. In this paper, we study total energy and reliability scheduling co-design problem for real-time cyber-physical systems. Total energy refers the sum of static and dynamic energy. Our goal is to minimize total energy while guaranteeing reliability constraints. We approach the problem from two directions based on the two different ways of guaranteeing the reliability of the tasks. The first approach aims at guaranteeing reliability at least as high as that of without speed scaling by reserving recovery job for each scaled down task. Heuristics have been used to guide the speed scaling and shutdown techniques that are used to lower total energy consumption while guaranteeing the reliability. The second way to guarantee the reliability of the tasks is to satisfy a known minimum reliability constraint for the tasks. The minimum reliable speed guarantees the reliability level of tasks, and is used as a constraint in the energy minimization problem. Both static and dynamic co-design methods are explored. Experimental results show that our methods are effective.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".