Migration Delay Awareness in a Self-Adaptive Balancing Scheme for HLA-Based Simulations
Bibliographic record
Abstract
Load balancing is a vital mechanism for improving the performance of distributed simulations or even for enabling their execution. A balancing technique has been designed in order to provide a balancing scheme for HLA-based simulations on non-dedicated resources. However, this technique lacks efficiency by producing large amounts of unnecessary federate migrations, so a self-adaptive mechanism has been introduced in the technique in order to correct its balancing responsiveness. As a drawback, the self-adaptation technique assumes that only the frequency of federate migrations represents the balancing efficiency. This leads the scheme to present static parameters regardless of the conditions of the environment, which in turn can restrict the balancing response to imbalances. Thus, awareness to migration delays is inserted into the self-adaptive balancing scheme in order that more precise and more realistic analysis of balancing efficiency can be enabled. Experiments have been conducted to show the performance gain of the proposed scheme when compared to the distributed and self-adaptive load balancing systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".