Control architecture of multiple-hop connections in IP over WDM networks
Bibliographic record
Abstract
In the existing functional definition of the optical network, the optical network primarily offers high bandwidth connectivity in the form of optical layer connections, where a connection is defined to be a fixed bandwidth circuit between two user network elements (also called clients). We propose to add IP routers packet processing function into the data plane of optical core networks, in order that transport service providers can provide multiple-hop connections between ISPs in addition to single-hop-only point-to-point lightpaths. Their benefits include optimized utilization of the bandwidth of lightpaths, more means to add and drop IP traffic, and flexibility of traffic engineering operations. We extend the functional models for IP-optical network interaction in the context of multiple-hop-enabled IP over WDM networks. The unified control and management supported by generalized MPLS is one of the underpinnings of the evolution from single-hop-only optical networks to multiple-hop-enabled IP over WDM networks. In order to support multiple-hop connections, we propose that the requests of connection establishment in the UNI should be extended to include the bandwidth and IP layer QoS requirements. Finally, the enhanced traffic engineering for transport service providers is discussed.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".