Criticality- and Requirement-Aware Bus Arbitration for Multi-Core Mixed Criticality Systems
Bibliographic record
Abstract
This work presents CArb, an arbiter for controlling accesses to the shared memory bus in multi-core mixed criticality systems. CArb is a requirement-aware arbiter that optimally allocates service to tasks based on their requirements. It is also criticality-aware since it incorporates criticality as a first-class principle in arbitration decisions. CArb supports any number of criticality levels and does not impose any restrictions on mapping tasks to processors. Hence, it operates in tandem with existing processor scheduling policies. In addition, CArb is able to dynamically adapt memory bus arbitration at run time to respond to increases in the monitored execution times of tasks. Utilizing this adaptation, CArb is able to offset these increases; hence, postpones the system need to switch to a degraded mode. We prototype CArb, and evaluate it with an avionics case-study from Honeywell as well as synthetic experiments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".