Investigation of data forwarding schemes for network resiliency in POX software defined networking controller
Bibliographic record
Abstract
Software defined networking (SDN) is an emerging networking architectural framework which aims to provide the complete separation of data forwarding plane and control plane. The two main benefits of SDN are lower cost and improved management. OpenFlow is a well‐known architecture that facilitates SDN. The core idea of OpenFlow is to control the switches or routers through programming from the centralised controller. The connection reliability is a major concern for network service providers to meet quality of service requirements. Hence, an investigation of network resiliency is required for this new paradigm. The resiliency is the network's ability to survive against attacks and other component failures. This study investigates and compares different data forwarding algorithms currently supported by the POX OpenFlow controller standards for network protection and restoration. A thorough investigation of existing approaches or standards in SDN not only is essential for the research community to better understand the topic, but also plays a crucial role in realising or improving network resiliency or protection and restoration in practice. The authors also provide the extension of one of the components in POX for improvement. The restoration scheme in the current POX components as well as in the modified component is evaluated and compared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".