Bibliographic record
Abstract
Software-Defined Networking (SDN) is a promising network architecture that proposes the decoupling of data and control planes.It uses a logically centralized controller powered by a global view to orchestrate the network, enabling innovation by shifting the task of network administration to network programming.Many SDN applications are designed to work autonomously without intervention, and thus they need to monitor the state of the network in order to take appropriate actions, and improve their future decisions.Furthermore, in the case of physically distributed SDN controllers, applications also need to exchange their views in order to build a global network view.This great shift in how networks are perceived gave birth to many new network applications design challenges.In this dissertation, we identify some of these challenges and study how they could affect the performance and security of SDN applications.In particular, we focus on the problem of inconsistent network view at the controllers and its impact on the network applications.We identify two key factors that can contribute to the inconsistent network view at the controllers: (1) network state collection; and (2) controllers' state distribution.We investigate different manifestations of the impact of network state collection and distribution on network applications performance and security.Moreover, we show that different network applications have different consistency requirements, and hence we introduce adaptive distributed SDN controllers as a solution to the controllers' state distribution.Adaptive controllers can tune their i
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".