Simulation-based Schedulability Assessment for Real-Time Systems
Bibliographic record
Abstract
Real-time systems not only require functional correctness, but also specific timing properties. Correct timing is especially challenging for hard real-time systems such as in medicine, avionics, and space industries, where missing a deadline can lead to catastrophic failure. A number of theories tackled this issue to determine whether a set of tasks running on a given architecture meets its timing constraints. One technique is schedulability analysis, which can provide guarantees for the timing behavior for a set of tasks. However, the use of schedulability tests involve an intrinsic amount of pessimism, which greatly reduces the number of configurations that can be considered as schedulable. This removes potentially promising system configurations from the task allocation optimization process, thereby reducing the quality of the final result. The aim of this paper is to overcome this limitation in the context of heterogeneous multiprocessor architectures. We propose a simulation-based approach to assess solutions discarded by a schedulability test, and include them in the optimization process. We tested our method on the optimization of the communication cost of a set of tasks scheduled on a quad core architecture, showing an improvement of up 11% when compared to the use of a schedulability test.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".