Efficiently Handling Process Overruns and Underruns in Real-Time Embedded Systems
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Bibliographic record
Abstract
Methods for handling process underruns and overruns when scheduling a set of real-time processes increase both system utilization and robustness in the presence of inaccurate estimates of the worst-case computations of real-time processes. In this paper, we present a method that efficiently re-computes latest start times for real time processes during run-time in the event that a real-time process is preempted or has completed (or overrun). The method effectively identifies which process latest start times will be affected by the preemption or completion of a process. Hence the method is able to effectively reduce real-time system overhead by selectively re-computing latest start times for the specific processes whose latest start times are changed by a process preemption or completion, as opposed to indiscriminately re-computing latest start times for all the 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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