Algorithm Package of AI-Driven SDN Controller-Switch Load Balancing Strategies
Bibliographic record
Abstract
Recently, Software-Defined Networking (SDN) is receiving much research attention due to its ability to decouple the data plane from the control architecture by associating the network switches to one (centralized) or more (distributed) controller(s). Traditionally, switches are assigned to the controllers in a static manner which results in under-utilization of the resources of the controllers and increased response delays to user requests. In this paper, we consider a practical load-balancing and agile scenario by formulating the dynamic associations of switches and controllers as an NP-hard optimization problem to minimize the maximum resource utilization of the controllers. Therefore, we propose an Ant Colony Optimization (ACO)-based algorithm to deal with the aforementioned request satisfiability issue in large SDN systems in polynomial-time. Furthermore, we envision a hybrid deep learning model consisting of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) structures to achieve near-optimal resource utilization for real-time SDN applications. Experimental results demonstrate that our customized CNN-GRU model outperforms the other techniques in terms of resource utilization (15% - 45% optimality gap) within a sianlficantly reduced computational running time (≤ 0.1s).
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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".