On scaling software-Defined Networking in wide-area networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Software-Defined Networking (SDN) has emerged as a promising direction for next-generation network design. Due to its clean-slate and highly flexible design, it is believed to be the foundational principle for designing network architectures and improving their flexibility, resilience, reliability, and security. As the technology matures, research in both industry and academia has designed a considerable number of tools to scale software-defined networks, in preparation for the wide deployment in wide-area networks. In this paper, we survey the mechanisms that can be used to address the scalability issues in software-defined wide-area networks. Starting from a successful distributed system, the Domain Name System, we discuss the essential elements to make a large scale network infrastructure scalable. Then, the existing technologies proposed in the literature are reviewed in three categories: scaling out/up the data plane and scaling the control plane. We conclude with possible research directions towards scaling software-defined wide-area networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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