A Formal Approach to RT-RTI Design Using Real Time DEVS
Bibliographic record
Abstract
High level architecture (HLA) is a well-known standard for constructing and supporting large-scale and complex distributed interactive simulation systems. HLA has several key components, among which; run-time infrastructure (RTI) is a crucial one as a service provider engine in HLA based simulation systems. Real Time extension of HLA/RTI became very important due to the necessity for the HLA to support simulation components with strict time constraints while interacting with each other. In fact, there have been a lot of research and concerns with regard to designing a high performance RT-RTI. In this paper, we propose a novel RT-RTI design approach that uses Real Time Discrete Event System Specification (RT-DEVS) formalism to model and simulate vital experimental frames. We are presenting a case study that demonstrates the usefulness of this formal approach in predicting the key design characteristics through designated simulation experiments. The simulation experimental results show that dynamic thread pool management with our load balancing strategies formed a key in improving the performance of RT-RTI in terms of serving tasks within their deadlines. Through our proposed formal design approach, we have seen an open area in finding the optimal RT-RTI design using RT-DEVS.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".