Topological Design of Two-Level Telecommunication Networks with Modular Switches
Bibliographic record
Abstract
In this article we propose a mixed 0-1 linear programming model for the topological network design problem with modular switches such as the ones that will be used in asynchronous transfer mode (ATM) frame relay and other broadband networks. The model includes the location of switches, their configuration with respect to ports and multiplexers, the design of an access network with a star topology, and a backbone network with a fixed topology (ring or tree). To obtain a solution, we propose a greedy heuristic that provides a good starting solution, and a tabu search heuristic to improve the solution. Finally, we present an example of the application of the heuristics and results for a set of randomly generated problems with up to 500 users and 30 potential switch sites. For the hundreds of problems generated, the tabu algorithm produced solutions that were, on average, within 1.5% of the optimal solution, and in the worst case within 4.95% of the optimal solution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".