Batching earliest deadline first scheduling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Investigates the trade-off in the dynamic scheduling of real-time tasks, between the frequency at which the scheduling algorithm is invoked, the size of the task set to which the scheduling (prioritization) policy is applied at every invocation, and the quality of the resulting schedules in terms of deadline compliance. We identify two classes of algorithms, one of which forms a batch of arrived tasks and which schedules and executes all tasks in a batch before considering other tasks that arrive in the meantime. The other class accounts for and schedules arrived tasks more frequently and applies the scheduling policy to all available tasks. We compare the performance of a batching and a non-batching technique, both of which apply an earliest-deadline-first (EDF) policy to prioritize tasks. An experimental evaluation of the proposed algorithms shows that our batching algorithms outperform their non-batching counterparts under tighter time constraints.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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