Optimized Dynamic Grid-Based DDM Protocol for Large-Scale Distributed Simulation Systems
Bibliographic record
Abstract
Data distribution management (DDM) is one of the six services provided by HLA/RTI as complementarities of declaration/interests management to provide a flexible and extensive mechanism for further throttling the data placed on the network and delivered to federates based on simulated entities' interests of data. DDM is of essential importance especially for large scale distributed simulations. In the past a few years, two main types of DDM protocols have been developed, named region-based methods and grid-based methods. However, all of these techniques have their obvious drawbacks, which affect their deployment in most applications that require high performance and low overhead. In our previous work, we have proposed a dynamic grid-based DDM scheme that shows a great potential when compared to both region-based and grid-based approaches. In this paper, we wish to improve our previous scheme, which we refer to as optimized dynamic grid-based DDM, to further reduce irrelevant data that might be received by simulated entities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".