An analysis of random cache effects on real-time multi-core scheduling algorithms
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Bibliographic record
Abstract
The effect of sharing the last-level cache (LLC) among cores in a multi-core system has not been thoroughly investigated especially in the design of efficient scheduling algorithms. And with the growing interest in random caches, which allow for an easier estimation of the worst-case execution time of tasks in critical real-time embedded systems, tools that analyse the sensitivity of workloads to sharing the LLC become necessary. In this paper, we extend a realtime multiprocessor scheduling simulator, SimSo, with a framework that incorporates a random cache model for multi-level caches to evaluate emerging scheduling algorithms under the influence of shared caches. A set of experiments were performed to study the behavior of workloads with respect to worst-case response time, average slack time, and maximum utilization, with varying cache designs under different scheduling algorithms.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it