Failure Recovery In SDN: A Segment Routing Based Link Protection Approach
Bibliographic record
Abstract
The present computer network has been evolved into a complex structure with a growing challenge to manage and scale modern day’s requirements. A new approach to tackle these difficulties is SDN, which empowers network with programmability and is designed to perform fine grained traffic forwarding decisions. However, similar to the need of traditional networks, fault tolerance is necessary to achieve high availability. In this thesis, we propose a link protection method based on the Segment Routing (SR) for rapid failure recovery in OpenFlow based SDN. Our proposed scheme performs local recovery at the switch level without the controller intervention, thus significantly reducing the total recovery time. Additionally, it reduces initial load on the controller while proactively computing the backup paths by minimizing the algorithm complexity. Moreover, memory efficiency is achieved by using a per-link protection with aggregated flow rules instead of traditional per-flow based protection mechanism. In Segment Routing, we may encounter the limitation on the size of the label stack, known as Segment List Depth (SLD). Therefore, we also propose an efficient label encoding algorithm to mitigate the SLD impact.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".