Exact ILP solution for the grooming problem in WDM ring networks
Bibliographic record
Abstract
We consider the problem of traffic grooming in second generation SONET/WDM rings with low-rate traffic circuits associated with a set of heterogeneous rate granularities. While networks are no longer limited by transmission bandwidth, the key issue in WDM network design has evolved towards the processing capabilities of electronic switches, routers and multiplexers. Therefore, we focus here on traffic grooming with minimum interconnecting equipment cost. We first formulate the problem as a generic integer linear programming (ILP) or a mixed integer linear programming (MILP) problem that encompasses several design specifications: UPSR vs. BLSR, non bifurcated vs. bifurcated flows, wavelength continuity constrained or free signal regeneration. Within the context of second generation SONET/WDM rings, we define the cost by a function of the number of transport blades, taking into account that the number of transport blades makes up a significant portion of the overall network cost. Using the CPLEX mixed ILP package, we next compare the optimal solutions of the ILP or MILP programs for different design assumptions, including the classical assumptions with a single hub where the lightpaths directly connect the hub to all other nodes.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".