A PSO based load-rebalance algorithm for task-matching in large scale heterogeneous computing systems
Bibliographic record
Abstract
The idea of utilizing nature inspired algorithms to find near optimal solutions to various real world NP complete optimization problems has been extensively explored by researchers. One such problem is the task matching problem in large heterogeneous distributed computing environments like Grids and Clouds. Researchers have explored Particle Swarm Optimization(PSO), which is branch of swarm intelligence, to find a near optimal solution for the task matching problem. In this work, I investigated the effectiveness of the smallest position value (SPV) technique in mapping the continuous version of the PSO algorithm to the task matching problem in a heterogeneous computing environment. The experimental evaluation demonstrated that the task matching generated by this technique will result in an imbalanced load distribution. In this work, I have therefore also designed a load-rebalance PSO heuristic (PSO-LR) that results in minimization of makespan and balanced utilization of the available compute nodes even in heterogeneous computing environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 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".