Mapping and Scheduling Mixed-Criticality Systems with On-Demand Redundancy
Bibliographic record
Abstract
Embedded systems in several domains such as avionics and automotive are subject to inspection from certification authorities. These authorities are interested in verifying the safety-critical aspects of a system and, typically, do not certify non-critical parts. The design of such Mixed-Criticality Systems (MCS) has received increasing attention in recent years. However, although MCS must be designed to overcome transient faults, their susceptibility to transient faults is often overlooked. In this paper, we consider the problem of mapping and scheduling efficient, certifiable MCS that can survive transient faults. We generalize previous MCS models and analysis to support On-Demand Redundancy (ODR). A task set transformation is proposed to generate a modified task set that supports various forms of ODR while satisfying reliability and certification requirements. The analysis is incorporated into a design space exploration algorithm that supports a wide range of fault-tolerance mechanisms and heterogeneous platforms. Experiments show that ODR can improve Quality of Service (QoS) provided to non-critical tasks by 29 percent on average, compared to lockstep execution. Moreover, combining several fault-tolerance mechanisms can lead to additional improvements in schedulability and QoS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".