Integrating I-DEVS and schedulability methods for analyzing real-time systems constraints
Bibliographic record
Abstract
The design of embedded real-time systems (RTS) is challenging due to the criticality of the timing constraints of these systems. Various informal and formal methods for RTS design have been proposed, both in the design space and the real-time execution at the hardware level, but many of these methods are not effective when the complexity of the system scales up. Here, we discuss a new method to integrate a modeling (and simulation) formalism that allows designing complex systems specifications for real-time constraints called Imprecise-DEVS (I-DEVS), and the mapping of such high-level models into a real-time task model. This method allows analyzing real-time constraints both at the high level of modeling as well as the low level of the tasks executed by the processing units and the Operating System. A new method to study the schedulability of the task models is proposed. The method provides a design analysis space from the model level, up to the individual tasks, with a focus on the schedulability of real-time constraints under transient overloading conditions.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".