Dynamic Load Balancing Using Grid Services for HLA-Based Simulations on Large-Scale Distributed Systems
Bibliographic record
Abstract
HLA-based simulations, as any distributed computing application, can undergo critical performance issues due to load imbalances on large-scale, heterogeneous, non-dedicated distributed systems. Such imbalances are produced by HLA simulation entities that can dynamically change their computation and communication load during their execution time, so an initial static load deployment is incapable of providing simulations complete and even distributed resources usage. Moreover, because the computing resources are non-dedicated, unknown external applications can generate load for any computing resource, increasing the imbalances' unpredictability. Thus, in order to re-allocate resources for an HLA simulation during its execution time, an hierarchical dynamic load balancing system is introduced. The system manages a simulation's workload by monitoring the distributed load through the MDS Grids' service; by identifying load imbalances according to a load sharing policy; by re-allocating resources according to defined policies; and by migrating federates through the GRAM Grids' service, a migration proxy, and peer-to-peer state transfer. By keeping the load evenly partitioned on the distributed system, such a devised system successfully improved the simulations' performance. The experimental results and comparative analyses between balanced and non-balanced simulations proved the efficiency of the proposed dynamic load balancing system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".