Exploring Source Routing as an Alternative Routing Approach in Wide Area Software-Defined Networks
Bibliographic record
Abstract
Software-Defined Networking (SDN) has been gaining increasing attention in both the research and the industry communities.Separating the control and data planes has brought many advantages such as greater control plane programmability, more vendor independence, possibility of network virtualization, and lower operational expenses.SDN deployments are possible in a variety of contexts: Enterprise networks, Datacenter networks, and Wide Area Networks (WANs).However, SDN raises several concerns for WANs including performance limitations due to the larger propagation delays, the increased controller work load due to a larger number of network elements, and concerns about performance impacts of the controller placement.This study attempts to address some of these issues by examining the effects of using source routing as an alternative to traditional distributed routing in SDN-based WANs.Our simulations and analysis show that source routing can bring significant gains in SDN performance in WANs, improve network scalability, and reduce the network performance sensitivity to controller placement.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".