An Efficient Adaptive Transmission Control Scheme for Large-Scale Distributed Simulation Systems
Bibliographic record
Abstract
Data Distribution Management (DDM) is one of the most critical component of any large-scale interactive distributed simulation systems. The aim of DDM is to reduce and control the volume of information exchanged among the simulated entities (federates) in a large-scale distributed simulation system. In order to fulfill its goal, a considerable amount of DDM messages needs to be exchanged within the simulation (federation). The question of whether each message should be sent immediately after it is generated or held until it can be grouped with other DDM messages needs to be investigated further. Our experimental results have shown that the total DDM time of a simulation varies considerably depending on which transmission strategy is used. Moreover, in the case of grouping, the DDM time depends on the size of the group. In this paper, we propose a novel DDM approach, which we refer to as Adaptive Grid-based (AGB) DDM. The AGB protocol is distinct from all existing DDM implementations, because it is able to predict the average amount of data generated in each time step of a simulation. Therefore, the AGB DDM approach controls a simulation running in the most appropriate mode to achieve a desired performance. This new DDM approach consists of two adaptive control parts: 1) the Adaptive Resource Allocation Control (ARAC) scheme and 2) the Adaptive Transmission Control (ATC) scheme. The focus of this paper is on the ATC scheme. We describe how to build a switching model to predict the average amount of DDM messages generated and how the ATC scheme uses this estimation result to optimize the overall DDM time. Our experimental results provide a clear evidence that the ATC scheme is able to achieve the best performance in DDM time when compared to all existing DDM protocols using an extensive set of experimental case studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".