Performance modeling of a grid-based Data Distribution Management protocol
Bibliographic record
Abstract
Multi-casting and Data Distribution Management (DDM) play a critical role in the design of future generations of large-scale distributed simulation systems, in which a large number of DDM messages are exchanged between the participating entities. Thus, communication overhead is one of the most challenging issues that needs to be studied and it must be investigated further whether each message should be sent immediately after it is generated during the simulation or held until it can be grouped with other DDM messages. Very little work has been done to answer these questions. 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 message grouping, the DDM time depends on the size of the group. In this paper, we propose a novel Adaptive Transmission Control (ATC) scheme for DDM. As opposed to previous DDM schemes, our proposed scheme has the ability to predict the average amount of DDM messages generated in each timestep of a simulation. Therefore, the ATC scheme is able to control a simulation running in the most appropriate mode to achieve a desired performance. In order to accomplish this goal, we will show how to use the switching model to predict the average amount of DDM messages generated in the grid-based DDM system. The experimental results are used to validate the correctness of the switching model and exhibit improved performance over previous schemes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".