Enduring Node Failures through Resilient Controller Placement for Software Defined Networks
Bibliographic record
Abstract
Software Defined Networking (SDN) is an emerging paradigm for network design and management. By providing network programmability and separation of control and data planes, SDN offers salient features such as simplified and centralized management and control, reduced complexity and accelerated innovation. However, SDN introduces new challenges that should be addressed properly in order to benefit from its unprecedented capabilities. Due to the (logically) centralized control in SDN, the resilience of the control plane has a great impact on the functioning of the whole system. In this case, resilient controller placement problem (how many controllers are needed and where to place them to provide higher reliability) is a hot research topic that affects the reliability and performance of SDN in Wide Area Networks (WANs). Thus, we define a resilient controller placement problem, which satisfies a set of constraints, some of which are missing in the existing solutions. The acquired results on real tier-1 US service provider network topologies demonstrate the effectiveness of the approach. This can give helpful insights to the network operators for designing or modifying their network topologies to enhance the resilience of the control plane in SDN.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".