Path selection with tunnel allocation in the optical Internet based on generalized MPLS architecture
Bibliographic record
Abstract
GMPLS was devised to be able to support multi-granularity traffic and bundling of wavelength channels in the optical domain. It has been a challenge to achieve an efficient and flexible use of the multi-granularity OXCs (MG-OXCs) in the optical next generation Internet which is assumed to deploy a generalized MPLS (GMPLS) based control plane. In this paper, a heuristic algorithm, capacity-balanced static tunnel allocation (CB-STA), is proposed for solving the problem of routing and wavelength assignment with tunneling (RWAT), which is aimed at facilitating an efficient use of bandwidth in the WDM networks with MG-OXCs. CB-STA allocates fiber and waveband tunnels into networks at the network planning stage, which requires each tunnel to have a fixed length and capacity-balanced characteristics, in order to increase the link utilization in the fiber and waveband switching layers. A comparison is made, using simulation, between CB-STA and a dynamic tunnel allocation scheme. Detailed discussions are provided.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".