Genetic algorithms with particle swarm optimization based mutation for distributed controller placement in SDNs
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Bibliographic record
Abstract
This paper proposes a distributed controller placement problem that finds out the pareto optimal solutions minimizing the switch-to-controller delay, controller-to-controller delay, and controller load imbalance for wide area software defined networks. We introduce a general model that not only considers the controller placements but also the switch assignments, so that this model can further be used to develop many other multi-objective optimization problems such as energy saving, controller migration, or NFV allocation. To solve this problem with huge search space without losing generality, we introduce a Multi-Objective Genetic Algorithm (MOGA) with a particle swarm optimization based mutation function. It maintains a pre-calculated global best position for each single objective, and choose the global best position of an objective that has the best accordance to a parent to guide the mutation of the parent. Evaluations show that our MOGA can generate a pareto frontier with a larger diversity toward the given global best positions in much shorter convergence time than a general MOGA.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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