N∶1 Protection Design for Minimizing OLTs in Resilient Dual-Homed Long-Reach Passive Optical Network
Bibliographic record
Abstract
Long-reach passive optical networks (LR-PONs) prove to be a suitable candidate for future broadband access networks. The longer reach of the feeder fiber in a LR-PON enables us to consolidate a large number of end users. The longer reach also eliminates a degree of electronic processing by eliminating the metro network and connecting the local exchanges (or the central offices) directly to a consolidated metro/core (MC) node. However, longer reach makes the feeder fiber more vulnerable to failures, and therefore, for resiliency purposes, a dual-homed architecture is proposed. For the usual case of 1+1 protection, the dual-homed secondary MC node would contain duplicate resources that would take over in the event of the failure of any of the individual working optical line terminals (OLTs) or the entire primary MC node in the case of a catastrophe. In this work we propose an N∶1 protection mechanism to reduce backup OLTs in a resilient dual-homed LR-PON deployment. We model the problem as an integer linear program and solve it for Irish and UK network deployments. Our results show that the percentage of backup OLTs can be reduced by 6 times for Ireland and by 4 times for the UK compared to a 1+1 protection deployment scenario.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".