Distributed dynamic balancing of communication load for large-scale HLA-based simulations
Bibliographic record
Abstract
In large-scale distributed simulations, communication aspects are highly significant due to their direct influence on performance. The High Level Architecture (HLA) provides services for managing such simulations and reducing their communication overhead. However, HLA does not present any solution for the communication latencies caused by the network distances among simulation elements. Several dynamic balancing schemes have been proposed attempting to provide a general best solution for the performance issues caused by computation and communication imbalances. Amongst these schemes, some just perform a limited redistribution of communication load. Based on a proximity analysis of federate interactions, a distributed dynamic scheme for balancing the communication load of HLA-based simulations is devised. The design of this distributed scheme aims at improving fault tolerance, decreasing communication and computation overload, and avoiding bottlenecks in the system. The distributed balancing system, organized in the hierarchical structure, monitors simulations, redistributes load, and migrates federates. Experiments have been realized to compare the proposed distributed scheme with a centralized scheme and to prove its effectiveness for large-scale 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".