Load Prediction in HLA-Based Distributed Simulation Using Holt's Variants
Why this work is in the frame
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Bibliographic record
Abstract
Due to the dependency of HLA-Based distributed simulations on the resources of distributed environments, simulations can face load imbalances and can suffer from low performance in terms of execution time. High-Level Architecture (HLA) is a framework that simplifies the implementation of distributed simulations, and, it has been built with dedicated resources in mind. As technology is nowadays shifting towards shared resources, the following two weaknesses have become apparent in HLA: managing federates and reacting towards load imbalances on shared resources. Moreover, a number of dynamic load management systems have been designed in order to provide a solution to enable a balanced simulation environment on shared resources. These systems use some specific techniques depending on certain simulation or load aspects, to perform the balancing task. Load prediction is one such technique that improves load redistribution heuristics by preventing load imbalances. In this work, we present a number of enhancements for a prediction technique and compare their efficiency. The proposed enhancements solve observed problems with Holt's implementations on dynamic load balancing systems for HLA-Based distributed simulations and provide better forecasting. As a result, these enhancements provide better forecasting for the load of the shared resources.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it