Effect of PSO Communication Topologies on Task Matching in Grid Computing
Bibliographic record
Abstract
The ability to solve large-scale problems efficiently is one of the intrinsic values of a grid system. To maximize the throughput of the grid system, matching the submitted tasks to suitable resources is essential. This problem has been identified as the task matching problem. The task matching problem has been studied extensively in the past. Among other nature-inspired algorithms, Particle Swarm Optimization (PSO) has been used more commonly for the task matching problem and has shown promising results. Throughout the literature, the global PSO (gbest) has been chosen as the standard topology, while few other works have considered the local best (ring) topology for the task matching problem. As a result, there is a lack of research investigating the effectiveness of other topologies for the task matching problem. This knowledge gap has motivated our research to elucidate the impact of different PSO topologies on the task matching problem. We observed that the fully connected topology performed best (makespan) in many sets of experiments, but only slightly. Additionally, it was evident from the results that the pyramid topology achieved a slight edge over the other topologies in terms of makespan when all experiments were considered. However, each topology worked better on some problems and not as well on other problems. In addition, the population size significantly impacts the balance between the exploration and the exploitation search process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".