Design of WDM networks under economy of scale pricing and shortest path routing
Bibliographic record
Abstract
Given a combination of unprotected and dedicated edge-disjoint path (1+1) protected connection requests and a finite set of fiber types, we consider the problem of allocating fibers on the links of a WDM network at minimum cost, such that all connection requests can be simultaneously realized. Each fiber type, is characterized by its capacity and its cost per unit length, where costs reflect an economy of scale. It is known that a solution induced by "simply" routing each unprotected (respectively 1+1 protected) connection along the shortest path (respectively shortest pair of edge-disjoint paths) minimizes the total wavelength mileage, but may not minimize the total fiber cost. In this paper, we quantify the increase in fiber cost due to shortest path routing. In particular, we prove that the total cost of a shortest path based solution is guaranteed to lie within a certain factor of the minimum possible cost. This leads also to the fact that shortest path routing is asymptotically cost-optimal for a large total number of connection requests. Furthermore, for sparse topologies, e.g., the ring, the ShuffleNet and the mesh(-torus), we show that shortest path routing is asymptotically cost-optimal in large-scale networks supporting all-to-all communication. En route, we prove that by shortest path routing we obtain a provably optimal solution to the linear programming (LP-) relaxation of the problem. We have thus presented a provably good upper bound and a lower bound on the total fiber cost, that can be computed in polynomial-time. These bounds can be used as benchmarks against which heuristic approaches are compared.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".