Fault-tolerant scheduling of multicore mixed-criticality systems under permanent failures
Bibliographic record
Abstract
Mixed-criticality systems are real-time systems that combine both safety-critical and non-critical applications. These systems have been gaining interest due to their practical applications and their adoption in standards in several domains. On the other hand, it is often overlooked in the research on mixed-criticality systems that these systems are still safety critical and must be able to operate even when the system's processors fail. In this paper, we present an approach to design multicore mixed-criticality systems that can survive permanent processor failures. Under the proposed work, critical tasks executing on the failing cores are migrated to other cores to allow them to continue execution. Space is made on the new cores by dropping non-critical tasks. Schedulability analysis is developed by extending the AMC-rtb analysis to support processor failures. The problem of finding a mixed-criticality system configuration on a multicore architecture is formulated as a Mixed Integer Linear Programming (MILP) problem. The MILP produces a system design that is schedulable on the underlying platform and tolerant to processor failures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".