A load-rebalance PSO heuristic for task matching in heterogeneous computing systems
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Bibliographic record
Abstract
The idea of utilizing nature inspired algorithms to find optimal solutions to various real world NP complete optimization problems has been extensively explored by researchers. One such problem is task matching problem in heterogeneous distributed computing environments like Grid and Cloud. Researchers have explored Swarm Intelligence algorithm, Particle Swarm Optimization (PSO), to find optimal solution for task matching problem. In this study, we investigate the effectiveness of smallest position value (SPV) technique in mapping continuous version of PSO algorithm to the task matching problem in a heterogeneous computing environment. We show that the task matching generated by this technique will result in in-efficient resource utilization. Thus, we present a novel load rebalance based particle swarm optimization heuristic (PSO-LR) for efficient load distribution among available compute nodes even in heterogeneous computing environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 it