Metaheuristic Optimization of Controller Placement in Software Defined Networks
Bibliographic record
Abstract
Introducing Software Defined Networking (SDN) controllers throughout the network enhances its scalability and management capabilities. Nonetheless, determining the optimal placement of these controllers within the network presents a challenging problem, because the controller placement decision must prioritize objectives such as minimizing latency, ensuring high reliability, minimizing hardware cost, and maximizing throughput of the network. Additionally, these controllers offer several advantages over traditional switches or routers, such as programmability, managing traffic flows, and providing alternative paths. The placement of the controllers significantly influence the robustness and resilience of the network. In this study, we provide a cutting-edge metaheuristic algorithm to solve the controller placement problem (CPP). This algorithm is applied to the integer linear programming (ILP) solution to improve the performance and reliability of the network by selecting optimal nodes of the network for controller placement, and the SDN-enabled network outperforms the state-of-the-art legacy network. The study formulates the maximum throughput, propagation delays, and controller cost mathematically in the collected network, which are then solved using the proposed algorithm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".