CLIP: Centrality-Led ILP Controller Placement for Software-Defined Networking
Bibliographic record
Abstract
Software-Defined Networking (SDN) revolutionizes network architectures by decoupling control and data planes, with controller placement critically impacting performance metrics. While existing state-of-the-art approaches focus on heuristic solutions or single-objective optimization for the Controller Placement Problem (CPP), this study advances the field through a rigorous mathematical formulation integrating both topological characteristics (distance matrices) and network centrality measures (betweenness centrality). We propose CLIP, an integer linear programming (ILP)-based solution that simultaneously optimizes for latency reduction, reliability enhancement, and number of controllers, addressing key limitations in current solutions. Experimental validation demonstrates that our SDN-enabled framework outperforms conventional legacy networks by 49.92-51.60% in latency reduction, while maintaining comparable deployment costs. These results establish a new benchmark for CPP optimization, particularly in scenarios requiring balanced multi-objective decision-making under real-world constraints. The findings provide network architects with a principled methodology for SDN deployment that surpasses current industry standards.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.005 | 0.001 |
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".