Multiple abstraction schemes for generalized virtual private switched networks
Bibliographic record
Abstract
An IP-VPN overlay over traditional L1/L2 transport networks has serious problems with scaling, not only with respect to routing but also the time it takes to provision these services. GVPN is a service that uses GMPLS as the common control plane to address these issues with features like client initiated signaling and auto-discovery. It allows routing over the access links, eliminating the O(n^2) client routing adjacency issue. In today's deployment, routing enables exchange of reachability information, which is not very useful for dynamic edge nodes requiring services on demand. Also a major obstacle to exploiting routing to its full potential is the provider's reluctance to expose the internals of the core network. In this piece, we try to explore the idea of enabling traffic-engineering capability to the edge routers using the concept of topology abstraction, which involves no preset resources. The study shows the different forms of abstractions that are possible with their pros and cons. We end our discussion prototyping two different abstraction schemes discussed in this article using a real lab setup. Our discussion for the most part is generic and could be applied to any L1 or L2 switched transport networks.
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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 | 0.003 | 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 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".