Tunneling techniques for end-to-end VPNs: generic deployment in an optical testbed environment
Bibliographic record
Abstract
Service providers today are constantly seeking to offer multiple services on a single common infrastructure. For instance, it is desirable sometimes to provide transport services transparently to data traffic encapsulated over different network layers. Tunneling is a technique for encapsulating a packet or frame within another packet of the same or a different network layer. One of the motivations for tunneling is bridging various heterogeneous networks that use different protocols for communication. Tunneling is also used for providing private and secure communications over a publicly shared network. This article investigates the interactions between different tunneling technologies in order to provide end-to-end virtual connectivity to end clients. Particularly, the article describes the technical details of the implementation of various layer-2 tunneling techniques-such as L2TP, GRE, and MPLS-based tunnels- in order to establish an end-to-end virtual connection-service as a concatenation of services offered by the different network domains along the path between end users.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".