Dimensioning networks of ROADM cluster nodes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Next-generation optical networks require high-degree, high-capacity reconfigurable optical add-drop multiplexer (ROADM) nodes and intelligent network planning schemes. We propose a cluster ROADM node design and a network dimensioning method that optimizes the resource utilization of optical networks with cluster nodes. The proposed ROADM cluster node offers a flexible add-drop rate, scaling to 100s of degrees, and a cost per degree similar to today’s ROADM. It disaggregates the cluster’s line and add-drop functions into different chassis. The low-cost node architecture is complemented by an order-based connection management algorithm that achieves better than 10 −4 blocking despite being equipped with less than 30% dilation in the cluster design. For an optical network with ROADM cluster nodes, we propose a network dimensioning scheme that proactively uses network knowledge to determine the optimum degree for ROADM nodes as demand increases. The results show a much-improved blocking rate, particularly at medium to high loading and an average of 3.1% increased utilization on each network’s fiber compared with reactive schemes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it