OPN02-2: Inter-Group Shared Protection (I-GSP): A Scalable Solution for Survivable WDM Networks
Bibliographic record
Abstract
The past studies for survivable routing suffers from the scalability problem when the number of nodes or connection requests grows in the network. In this proposal, a novel path based shared protection framework namely Inter-Group Shared protection (I-GSP) is developed such that the traffic matrix can be divided into multiple protection groups (PGs) based on specific grouping policy. This novel scheme not only overcomes the scalability problem but also provides an upper bound on the affected working paths in case of link failure in the network. Experiment results show that I-GSP based integer linear programming model solves the networks in a reasonable amount of time for which a regular integer linear programming formulation becomes computationally intractable. For most of the cases the performance gap between the optimal solution and the proposed I-GSP ranges between (2-16)%. The proposed optimization model yields a scalable and near-optimal solution for the capacity planning in the survivable optical 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".