An engineering approach to decomposing end-to-end delays on a distributed real-time system
Why this work is in the frame
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Bibliographic record
Abstract
We propose an adequate engineering technique for decomposing end-to-end delays in distributed real time systems. Our technique greatly simplifies the real time system design process by turning a global distributed scheduling problem into a set of single processor scheduling problems with local deadlines. The deadline decomposition is done using critical scaling factor (J. Lehoczky et al., 1989) as a schedulability metric. As the problem is extremely hard in general, we develop an approximate technique using a simple linear response time model to generate a quick initial solution. We then go on to show how the initial solution helps us identify the bottlenecks, and then use that knowledge to iteratively fine tune the initial solution. The end result is a practical engineering technique to decomposing end-to-end deadlines.
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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) | 0.000 | 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 | 0.001 |
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