Partition modeling and optimization of ARINC 653 operating systems in the context of IMA
Bibliographic record
Abstract
The adoption of Integrated Modular Avionics (IMA) architecture is a technological trend in the avionics industry due to its capability of supporting space and temporal partitioning, which is mandatory for systems with mixed criticality. However, combining partition allocation and schedule design for applications sharing hardware, software, and communication resources of the same computing platform while assuring temporal behavior is a complex task that requires adequate tools for system design and integration. This paper presents the main features of a model that has been developed for simultaneous partition allocation and schedule design, which allows for automatic adjustment of both applications distribution over the partitions and scheduling parameters toward performance optimization. In the proposed model, all the variables are integer and all constraints are formulated via linear equalities and inequalities. Therefore, this problem can be efficiently solved by many existing mixed integer linear programming algorithms. A set of timing constraints at both partition and task levels are established, and different optimization objective functions are provided. The results of a case study show that, if a solution exists, the proposed model can achieve a global optimum while guaranteeing that all the constraints are met.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".