Dynamic partitioning of distributed virtual simulations for reducing communication load
Bibliographic record
Abstract
HLA-based simulations can experience performance degradation due to communication latencies between simulation federates, which generate significant cumulative overhead. Even though the HLA standard provides mechanisms to decrease the misuse of network resources, it does not present any tool to diminish the communication latencies between interactive federates. Moreover, the interaction dependencies can be predicted before simulations are initiated, but such predictions relies on the determinism of simulations, producing erroneous balancing when simulations change their load dynamically. Thus, an hierarchical three-phase dynamic communication load balancing scheme is devised to react to run-time load changes, so the scheme performs constant, periodical monitoring of resources, re-distribution of load, and migration of federates. The balancing system reorganizes the distribution of large-scale HLA-based simulations, so the communication latencies are minimized, increasing the parallelism of the distributed simulations and leading to a performance improvement. Experiments were realized to measure the benefits of the scheme, and through comparative analyses, the balancing scheme presented considerable performance improvement to HLA-based simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".