Schedulability Analysis for Real-Time Task Set on Resource with Performance Degradation and Dual-Level Periodic Rejuvenations
Bibliographic record
Abstract
Researches in real-time scheduling often assume that the performance of a computing resource does not change overtime. However, as system softwares and system architectures become increasingly complex, resource performance degradation over time becomes more evident. In this paper, we study the schedulability of a hard real-time task set on a resource which has performance degradation over time with a known pattern and use both cold and warm periodic rejuvenations as countermeasures. Such resource model is referred to as P2D-resource model for performance degradation and periodic rejuvenation with dual-levels. In this paper, we study (1) the formal specification of the P2D-resource model, (2) P2D-resource supply analysis, and (3) task set utilization bounds of a P2D-resource under Earliest Deadline First (EDF) and Rate Monotonic (RM) scheduling policies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".