Enhancing the effectiveness of traffic engineering in hybrid SDN
Bibliographic record
Abstract
A lot of researches exploit the flexibility of Software-Defined Networking (SDN) to conduct traffic engineering in order to improve network performance and enhance robustness to failures. As the upgrade of a traditional network to a full SDN deployment is an incremental process, the coexistence of SDN switches and legacy switches forms a hybrid SDN. Due to the different forwarding characteristics of these switches, it is essential to coordinate the forwarding of SDN control and distributed routing to avoid inconsistency and achieve high network utilization. In this paper, we note that the effectiveness of traffic engineering in hybrid SDN strongly depends on both the structures of forwarding graphs and traffic distribution, while existing approaches mainly focus on the latter. We first define the consistent forwarding graph, and then construct forwarding graphs with potential high throughput for effective traffic engineering, while maintaining forwarding consistency. The evaluation results show that the proposed forwarding graph construction approach improves network throughput and achieves better load balancing compared with existing simple forwarding path constructing approaches, especially with more fraction of SDN deployment.
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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 | 0.001 | 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 | 0.001 | 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".