An Efficient Periodic Resource Supply Model for Workloads with Transient Overloads
Bibliographic record
Abstract
Real-time applications have deadline constraints. The system should provision sufficient resources for the application to meet the deadlines, and use supply and demand bound functions to analyze the schedulability of workloads. The concept of the demand bound function describes the upper bound on the resources required by the application, while the supply-bound function specifies the lower bound on the resources supplied to the tasks. If the system provides fewer resources than required, the application will experience an overload. Most work concentrates on designing systems that cannot experience short periods of overloads. This work explores resource provisioning for control applications that can tolerate overloads. It introduces analysis techniques for supply and demand bound functions that specifically consider overloads and delays in a periodic resource model. With this extended model, the work addresses three problems: (1) determine the worst-case delay for a given resource demand and supply under a periodic resource model, (2) find a periodic resource supply for a given workload and worst-case tolerable delay, and (3) for a control system with a given robustness criterion, identify a periodic resource supply with a worst-case delay.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".