A Survey and a Layered Taxonomy of Software-Defined Networking
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 recently gained unprecedented attention from industry and research communities, and it seems unlikely that this will be attenuated in the near future. The ideas brought by SDN, although often described as a “revolutionary paradigm shift” in networking, are not completely new since they have their foundations in programmable networks and control-data plane separation projects. SDN promises simplified network management by enabling network automation, fostering innovation through programmability, and decreasing CAPEX and OPEX by reducing costs and power consumption. In this paper, we aim at analyzing and categorizing a number of relevant research works toward realizing SDN promises. We first provide an overview on SDN roots and then describe the architecture underlying SDN and its main components. Thereafter, we present existing SDN-related taxonomies and propose a taxonomy that classifies the reviewed research works and brings relevant research directions into focus. We dedicate the second part of this paper to studying and comparing the current SDN-related research initiatives and describe the main issues that may arise due to the adoption of SDN. Furthermore, we review several domains where the use of SDN shows promising results. We also summarize some foreseeable future research challenges.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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