Dynamic On-Demand Virtual Extensible LAN Tunnels via Software-Defined Wide Area Networks
Bibliographic record
Abstract
The world of information and communications technologies continues to evolve at an exponential rate, not only inaugurating numerous breakthroughs in research, but also changing the perception of interconnectivity and exchange of information. The increasing popularity of software-defined networks (SDN) and the related technologies have shattered the realm of corporate infrastructures. The traditional approach for employees to access the corporate headquarter data centers has experienced challenges due to the significant traffic volume and delay, as more services have been moved to the cloud, e.g., Software-as-a-Service (SaaS). Tunnel-splitting can mitigate the problem, but it is mostly static. The paper proposed a dynamic on-demand tunnels approach based on Extensible LAN Tunnels (VXLAN) and SDN. The primary objectives are to reduce the load on corporate network and delay for users to access SaaS. We conducted experiments for feasibility study using Mininet and the results showed that the delay could be significantly reduced and the approach allows for a single point of policy management, which it still preserved the benefit of split tunnels.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".