Influence of modularity and economy-of-scale effects on design of mesh-restorable DWDM networks
Bibliographic record
Abstract
This work is motivated by interest in restorable mesh architectures for WDM optical networking DWDM technology is expected to create an extremely modular capacity-planning situation and to produce potentially strong nonlinear economy-of-scale effects in capacity. How will this influence the design of cost-optimized mesh-restorable networks? Will it be essential to do true modular design optimization, or will the traditional rounding-up procedure still be adequate? Can a true modular design method exploit these effects for capital cost savings in the network design? What influence would strong modularity and economy-of-scale have on the evolution of the fiber facilities graph topology for these networks? We address these questions with three mathematical programming formulations that allow a comparative study of these issues in terms of the cost and architectural differences between networks designed with different treatments of the modularity issue. Results show that there are worthwhile savings to be had by bringing modularity aspects directly into the basic design formulation, rather than postmodularizing a continuous integer result, as done in most prior practice. The most significant research finding may be the demonstration of topology reduction (or paring down of the facilities graph) arising spontaneously in optimized designs under the combined effects of high modularity and economy-of-scale. This is the first quantitative indication and explanation of why less highly connected graph topologies may be preferred (at least from an economic standpoint) in future WDM networks, even though the spare capacity efficiency for mesh-based restoration is improved by higher connectivity.
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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 | 0.002 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".