Analysis of Real-Time Systems Sensitivity to Transient Faults Using MicroC Kernel
Bibliographic record
Abstract
Increasing complexity of safety-critical systems that support real-time multitasking applications requests the concurrency management offered by real-time operating systems (RTOS). Real-time systems can suffer severe consequences if the functional as well as the time specifications are not met. In addition, real-time systems are subject to transient errors originating from several sources, including the impact of high energy particles on sensitive areas of integrated circuits. Therefore, the evaluation of the sensitivity of RTOS to transient faults is a major issue. This paper explores sensitivity of RTOS kernels in safety-critical systems. We characterize and analyze the consequences of transient faults on key components of the kernel of MicroC, a popular RTOS. We specifically focus on its task scheduling and context switching modules. Classes of fault syndromes specific to safety-critical real-time systems are identified. Results reported in this paper demonstrate that 34% of faults that affect the scheduling and context switching functions led to scheduling dysfunctions. This represents an important fraction of faults that cannot be ignored during the design phase of safety-critical applications running under an RTOS
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".