Creating logical zones for hierarchical traffic engineering optimization in SDN-empowered 5G
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 performs traffic engineering (TE) through a central SDN controller. Centralized TE is impractical when the network becomes too large in size or in loading. Zone-based hierarchical TE comes into play under this circumstance, where the TE problem is decomposed into sub problems related to zones and tackled collectively by local zone controllers. As an integral part of this TE approach, zoning was studied in a very limited context. Existing solutions generate geographic zones and suit only arc-model TE optimization. In this paper, we advance the state of the art by proposing logical zones to support path-model TE optimization. Logical zones are created by coupling traffic flows to zone controllers. We mathematically formulate the zoning problem in different cases, where the number of available controllers is equal to, or larger than that of zones required. These problems are NP hard. We develop novel heuristic solutions and present comparative simulation study.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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