Handling Overruns and Underruns of Real-Time Processes With Precedence and Exclusion Relations Using a Pre-Run-Time Schedule
Why this work is in the frame
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Bibliographic record
Abstract
Many embedded systems applications have hard timing requirements where real-time processes with precedence and exclusion relations must be completed before specified deadlines. This requires that the worst-case computation times of the real-time processes be estimated with sufficient precision during system design, which sometimes can be difficult in practice. If the actual computation time of a real-time process during run-time exceeds the estimated worst-case computation time, an overrun will occur, which may cause the real-time process to not only miss its own deadline, but also cause a cascade of other real-time processes to also miss their deadline, possibly resulting in total system failure. However, if the actual computation time of a real-time process during run-time is less than the estimated worst-case computation time, an underrun will occur, which may result in under-utilization of system resources. This paper describes a method for handling underruns and overruns when scheduling a set of real-time processes with precedence and exclusion relations using a pre-run-time schedule. The technique effectively tracks and utilizes unused processor time resources to reduce the chances of missing real-time process deadlines, thereby providing the capability to significantly increase both system utilization and system robustness in the presence of inaccurate estimates of the worst-case computation times of real-time processes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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