Isochronous Execution Models for High-Assurance Real-Time Systems
Bibliographic record
Abstract
To counter the exorbitant cost of developing certifiable safety-critical software, we propose task execution models that realize an architectural mitigation-based approach to achieving high integrity and highly predictable safety-critical systems. Our premise is that high design assurance levels (DALs) for a software component may be achieved as follows: each system operation that needs a high assurance level will be handled by a software component that has high performance/quality-of-service on average, but built at a lower assurance level, and this component is “monitored” by one (or more) simple component(s) that is (are) predictable, may have lower QoS, but is (are) built at the highest assurance level. The components associated with a software function would run isochronously on separate processors. We present a suite of such isochronous allocation and scheduling problems with varying levels of generality, along with their solutions. We extend our results to the recurrent task model, where tasks should complete before their deadlines.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".