Performance Analysis of an Adaptive Dynamic Grid-Based Approach to Data Distribution Management
Why this work is in the frame
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Bibliographic record
Abstract
Data distribution management (DDM) plays a key role in traffic volume control of large-scale distributed simulations. In recent years, several solutions have been devised to make DDM more efficient and adaptive to different traffic conditions. Examples of such systems include region-based, fixed grid-based, hybrid, and dynamic grid-based (DGB) schemes. However, less effort has been made to improve the processing performance of DDM techniques. This paper presents a novel DDM scheme called the adaptive dynamic grid-based (ADGB) scheme that optimizes DDM time through analysis of matching performance. ADGB uses an advertising scheme in which information about the target cell involved in the process of matching subscribers to publishers is known in advance. An important concept known as distribution rate (DR) is devised. DR represents the relative processing load and traffic volume generated at each federate. The matching performance and DR are used as part of the ADGB method to select, throughout the simulation, the devised advertisement scheme that achieves maximum gain with acceptable network traffic overhead. Performance estimation and analysis of ADGB have shown that given an ideal matching probability, an efficiency gain of a maximum of 66% over the DGB scheme can be achieved. The novelty of the ADGB scheme is its focus on improving performance, an important (and often forgotten) goal of DDM strategies
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it