Degree-based Balanced Clustering for Large-Scale Software Defined Networks
Bibliographic record
Abstract
The Controller Placement Problem (CPP), in Software Defined Networks (SDNs), deals with placing an optimal number of controllers. The CPP aims to maximize the network throughput and minimize different latencies like flow-setup latency and route-synchronization latency. In recent years, many solutions to the CPP have been proposed- some approaches work with a single parameter like the average propagation delay of a network, reliability, load balancing, etc., while some other approaches provide exhaustive solutions which optimize multiple parameters. However, very few researches propose non-exhaustive solutions which simultaneously optimize more than one parameter. We propose a novel controller placement algorithm which clusters the SDNs in polynomial time complexity. Our proposed algorithm Degree-based Balanced Clustering (DBC) minimizes overall flow-setup latency as well as route-synchronization latency and balances the loads of the controllers at the same time. DBC divides a network into several clusters, places a controller in each cluster, and also selects an optimal number of controllers. Simulation results suggest that DBC outperforms existing state-of-the-art algorithms in terms of different latencies and also performs load balancing among the controllers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".