A comparison of compositional schedulability analysis techniques for hierarchical real-time systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Schedulability analysis of hierarchical real-time embedded systems involves defining interfaces that represent the underlying system faithfully and then compositionally analyzing those interfaces. Whereas commonly used abstractions, such as periodic and sporadic tasks and their interfaces, are simple and well studied, results for more complex and expressive abstractions and interfaces based on task graphs and automata are limited. One contributory factor may be the hardness of compositional schedulability analysis with task graphs and automata. Recently, conditional task models, such as the recurring branching task model, have been introduced with the goal of reaching a middle ground in the trade-off between expressivity and ease of analysis. Consequently, techniques for compositional analysis with conditional models have also been proposed, and each offer different advantages. In this work, we revisit those techniques, compare their advantages using an automotive case study, and identify limitations that would need to be addressed before adopting these techniques for use with real-world problems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it