Genetic algorithms with particle swarm optimization based mutation for distributed controller placement in SDNs
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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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 | 0.002 |
| 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 | 0.001 |
| 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".