Greening the multi-granular optical transport network design under the optical reach constraint
Bibliographic record
Abstract
Significant portion of the energy consumption of the optical networks is expected to be in the transport segment. Besides its improved bandwidth utilization advantage, multi-granular switching concept further helps rectifying the energy bottleneck problem in the backbone. One of the important challenges faced by the multi-granular optical networks is the optical reach enforcement. In this paper, we compare the multi-granular optical network design to the conventional Wavelength Division Multiplexing (WDM)-based network design by enforcing the optical reach limitation as a design constraint. We introduce the heuristics to solve the Routing and Multi-Granular Path Assignment (RMGPA) problem. Our simulation results show that multi-granular optical network design outperforms the WDM-based network design in terms of Operational Expenditure (Opex) as it significantly reduces the power consumption in the backbone. Furthermore, through simulations, we show that the green multi-granular design is efficient in terms of the Capital Expenditure (Capex) as the network cost is also degraded.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".