Optimization Placement for SDN Controller: Bell Canada as a Case Study
Bibliographic record
Abstract
The tremendous proliferation of data traffic has been a key motivator for the upgrading of traditional IP networks One new conceptual model that has been developed for redesigning and managing communication networks is software-defined networking (SDN). The main premise behind SDN is the decoupling of the control and data planes, which enables the centralization of the control plane and the programmability of the data plane. Despite these advantages, the use of SDN remains challenging with respect to a number of aspects, such as finding optimal locations for SDN controllers in a wide area network (WAN) and determining the effective number of controllers. The work presented in this thesis addresses these challenges through two proposed strategies for dealing with the SDN controller placement problem. The Bell Canada WAN was considered as a case study: the network was examined, and the modeled procedures for determining the best location for SDN controllers were applied with the goal of enhancing the quality of service (QoS) and minimizing global latency. The simulations conducted as a means of validating and comparing the performance of the two models produced consistent results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".