Creating logical zones for hierarchical traffic engineering optimization in SDN-empowered 5G
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".