DEVS Approach to Real-time RTI Design for Large-scale Distributed Simulation Systems
Bibliographic record
Abstract
HLA/RTI is one of the dominant middleware for large-scale distributed simulation. However, traditional RTI design and resulting implementations have been facing several difficulties, especially in satisfying the requirements for real-time distributed simulations. Many improved or novel RTI designs have been proposed in recent years, which aim to improve the real-time capabilities of RTI systems. Such designs are in general non-formalized, and the realizations of the designs are highly time-consuming and error-prone practices. In this paper, we propose a formal real-time RTI (RT-RTI) design approach using Discrete Event System Specification (DEVS). We discuss the feasibility of using DEVS and, as an additional step, we consider the case study of two recently proposed RT-RTI designs through a formalized DEVS model system. Our focus is how a DEVS component-based formalized design approach can predict some of the key design factors before the design is realized, or can further validate and consolidate realized RT-RTI designs.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".