Zoning for hierarchical network optimization in software defined 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) decouples control plane functionality from the data plane and features the presence of programmable dumb network devices, which have no or little intelligence and take control commands from a central controller at the control plane. The central controller is responsible for controlling data plane hardware and optimizing network operation. Centralized network optimization and control is impractical or infeasible when the network becomes too large in size or loading. Distributed network optimization comes into play under this circumstance. Fully distributed network optimization requires local intelligence at individual network elements, against the basic concept of SDN. In this paper we consider SDN-friendly zone-based distributed network optimization and studies the integral network zoning problem, that is, how to group network elements into zones such as to minimize the overhead of distributed network optimization. We give a mathematical formulation of the problem and show that it is NP complete. We then present three heuristic solutions and evaluate their performance through simulation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it