Efficient Load Balancing Schemes for Large-Scale Real-Time HLA/RTI Based Distributed Simulations
Bibliographic record
Abstract
The real-time extension of high level architecture (HLA) is very essential and useful for many large-scale distributed simulation systems. Most previous attempts to design the real-time run time infrastructure (RT-RTI) have enabled the usage of supported scheduling and prioritization services from underlying real-time operating systems (RTOSs) augmented by communication QoS mechanisms. In this paper, we wish to build on this functionality by proposing an algorithm that differentiates services processing within the RTI itself by incorporating resources load balancing mechanisms with several scheduling and allocation policies. We focus our efforts on making the RTI's internal operations organized and well suited to the tasks and services it will be providing throughout the lifetime of HLA-compliant federations. We discuss our load balancing strategies and demonstrate through our analytical performance evaluation and simulation experiments that our proposed real-time RTI framework exhibits a better performance in terms of the number of tasks served within deadlines compared with existing real-time RTI frameworks.
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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 | 0.003 | 0.001 |
| 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) | 0.001 | 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 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".