Bi-Objective simplified swarm optimization for fog computing task scheduling
Bibliographic record
Abstract
In the face of burgeoning data volumes, latency issues present a formidable challenge to cloud computing. This problem has been strategically tackled through the advent of fog computing, shifting computations from central cloud data centers to local fog devices. This process minimizes data transmission to distant servers, resulting in significant cost savings and instantaneous responses for users. Despite the urgency of many fog computing applications, existing research falls short in providing time-effective and tailored algorithms for fog computing task scheduling. To bridge this gap, we introduce a unique local search mechanism, Card Sorting Local Search (CSLS), that augments the non-dominated solutions found by the Bi-objective Simplified Swarm Optimization (BSSO). We further propose Fast Elite Selecting (FES), a ground-breaking one-front non-dominated sorting method that curtails the time complexity of non-dominated sorting processes. By integrating BSSO, CSLS, and FES, we are unveiling a novel algorithm, Elite Swarm Simplified Optimization (EliteSSO), specifically developed to conquer time-efficiency and non-dominated solution issues, predominantly in large-scale fog computing task scheduling conundrums. Computational evidence reveals that our proposed algorithm is both highly efficient in terms of time and exceedingly effective, outstripping other algorithms on a significant scale.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".