Simulator for Scheduling Real-Time Systems with Reduced Power Consumption
Bibliographic record
Abstract
Optimum resources utilization in computing devices especially power is among the prime areas of research from the very beginning of computer systems. However, its importance in the current era has been significantly increased due to the diverse nature of devices and their real time applications. On the other hand, paradigm is shifting towards sustainable resources that are green/environment friendly (low emission) in nature and produce relatively low energy/power. Real time systems (RTS) are relatively power-hungry due to their time constrained nature. So, there is room to investigate the scheduling algorithms (schedulers) with minimum (low) power consumption. On the other hand simulators are the software that mimic the real time environment for various parameter testing without actual implementation that could be costly as well as complex to build in the beginning. In this study, we are intended to develop a simulator for scheduling Real-Time Systems (RTS) with Reduced Power Consumptions (RPC). That is potentially an environment where various algorithms can be tested over different case studies to examine their performance pertaining RPC for RTS.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".