Nonutilization bounds and feasible regions for arbitrary fixed-priority policies
Bibliographic record
Abstract
Prior research on schedulability bounds focused primarily on bounding utilization/ as a means to meet deadline constraints. Nontrivial bounds were found for a handful of scheduling policies in which utilization is directly related to the ability of the policy to meet deadlines. Examples include rate-monotonic, deadline-monotonic, and EDF scheduling. For most other scheduling policies, however, utilization is not correlated with schedulability. For example, shortest-job-first can miss deadlines at an arbitrarily low utilization. This raises the question of whether or not some other nonutilization-based metric might be more indicative of schedulability in those cases. This article answers the above question positively by extending the notion of schedulability bounds, in a uniform manner, to arbitrary (fixed) priorities and nonutilization metrics. We present a simple function that generates the schedulability metric to be bounded from the definition of a fixed-priority scheduling policy, and derive a nontrivial schedulability bound on that metric for aperiodic tasks. It is shown that the generated metrics and bounds are valid in that no deadline misses occur when these bounds are not violated. This result allows efficient real-time admission control to be performed in systems with arbitrary fixed-priority scheduling policies. As an example, we illustrate applying schedulability bounds for admission control to shortest-job-first and velocity-monotonic scheduling. While the proposed nonutilization bounds and feasible regions are derived for fixed-priority scheduling policies, the authors are investigating extensions of the results to dynamic-priority scheduling.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".