Real-time benchmark set synthesis based on pWCET estimation and bounded hyper-periods
Bibliographic record
Abstract
In evaluating performance, schedulability, and energy efficiency metrics for real-time systems, numerous algorithms have been proposed to construct synthetic tasksets for simulation. The resulting taskset characteristics should ideally reflect real workloads while the algorithms generating these tasksets should be efficient. Any experimentation using these tasksets will highly depend on their properties. Current approaches construct the sets by choosing taskset periods and utilisation from statistical distributions and compute the task worst case execution times accordingly. Tasks are generated through timed loops or matrix operations up to the specified task WCET. At times, the taskset hyper-period is bounded to minimise simulation interval through selected assignment of task periods. However, tasks which burn processor cycles through loops and matrix operations do not always reflect realistic task loads. In this paper, we propose a methodology for generating realistic tasksets based on available embedded benchmarks. We extend on previous work and propose new algorithms: CPA-AU/DU (Compute-Propagate-Adjust Ascending/Descending Utilisation) which efficiently pair taskset WCETs with selected discrete periods. Our tasksets have bounded and feasible simulation interval and meet desired total utilisation with minimum digression errors. We also show that our algorithms run in polynomial time.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".