Group shared protection (GSP): a scalable solution for spare capacity reconfiguration in mesh WDM networks
Bibliographic record
Abstract
This paper proposes a novel framework of shared protection, namely group shared protection (GSP), in mesh wavelength division multiplexing (WDM) networks with dynamically arriving connection requests. Based on the (M:N)/sup n/ control architecture, GSP has n mutually independent protection groups, each of which contains N SRLG-disjoint working paths protected by M protection paths. Due to the SRLG-disjointedness of the working paths in each protection group, GSP not only allows the spare capacity to be totally sharable among the corresponding working paths, but also reduces the number of working paths affected due to a single link failure. Based on the framework, an integer linear program (ILP) formulation that can optimally reconfigure the spare capacity for a specific protection group whenever a working-protection path-pair joins is proposed. Two heuristics namely link-shared protection (LSP) and ring-shared protection (RSP) are introduced for further compromising the performance and the computational complexity. The proposed schemes are compared with a reported one, namely successive survivable routing (SSR). The experimental results show that LSP, RSP and SSR yield similar performance in terms of resource sharing, whereas ILP outperforms all of them by (6-16%). Due to the limited number of working paths in each protection group, ILP can handle a dynamically arriving connection request in a reasonable amount of time. Also, we find that the number of affected working paths in GSP is about half of that in SSR. We conclude that GSP provides a scalable and efficient solution for dynamic spare capacity reconfiguration following the (M:N)/sup n/ control architecture.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".