Dynamic load redistribution based on migration latency analysis for distributed virtual simulations
Bibliographic record
Abstract
Distributed virtual simulations deployed on shared resources can frequently undergo loss of performance due to external background load, improper placement of simulation entities, or dynamic simulation load changes. The High Level Architecture (HLA) was designed as a solution for coordinating the execution of distributed simulations. Even though this framework offers management services to organize such simulations, it does not provide mechanisms for detecting and controlling load imbalances. Several balancing approaches have been designed aiming at a generic scheme for solving load imbalance issues of distributed simulations, but these approaches are concerned with issues of specific simulation applications or are unaware of environment characteristics. To overcome such limitations, a dynamic, distributed balancing scheme has been developed. However, the scheme is not aware of federate migration latencies. Since migration latency directly influences balancing efficiency and responsiveness, a redistribution scheme is proposed to measure migration delays and use such delays in the balancing algorithm to determine load deployment changes. These delays are used in a cost function that determines the redistribution behaviour of the balancing scheme. Experiments have been performed to analyze the performance gain of the proposed scheme when migration procedures introduce costly latencies into 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".