Prediction-Based Switch Migration Scheduling for SDN Load Balancing
Why this work is in the frame
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Bibliographic record
Abstract
Distributed architectures of the SDN control plane require a careful design for balancing the load among controllers. Solutions proposed for SDN load balancing usually use switch migration operations. However, an efficient switch migration means triggering the operation at the right moment, and judiciously choosing the migrated switch and the destination controller. Here, we propose a switch migration scheduling algorithm to improve the migration efficiency, and ensure load balancing between controllers. Our algorithm uses a multi-step ARIMA forecasting model to predict the long-term controllers load. When an overload is predicted, a switch migration operation is scheduled in advance. After validating the accuracy of the ARIMA forecasting model, we evaluated the performance of the algorithm by analyzing the response time of controllers. Numerical results confirm the performance of the proposed algorithm.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it