Exact planning of GMPLS-based transport networks with conversion and regeneration capabilities
Bibliographic record
Abstract
With the ever-increasing traffic in WDM-based transport networks, the development of GMPLS (or multi-granular)-based transport networks becomes essential to avoid the cost explosion of OXCs and ROADMs. This paper addresses the optimal planning problem of the GMPLS-based transport network by (1) considering the whole traffic hierarchy defined in GMPLS; (2) allowing the optical signal conversion at all granularity levels; (3) imposing the optical reach constraint on the length of all-optical paths. We call such a planning problem the Routing and Multi-Granular Paths Assignment (RMGPA). The objective of the problem is to minimize the total weighted port count in the transport network. The RMGPA problem is formulated as a Mixed Integer Linear Programming (MILP) model. Due to the computational complexity of the problem, the MILP model is solved for small-sized planning problems. The solutions of the MILP model are used as valuable quality references for previously developed sub-optimum methods, which we have proposed in a previous work to solve large-sized planning problems in a reasonable amount of time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".