Multi-Granular Optical Transport Network design with dual power state
Bibliographic record
Abstract
Bandwidth utilization and management advancements of Multi-Granular Optical Transport Networks (MG-OTN) have been shown to couple with power savings through lowered port counts. In this paper, we propose energy-efficient design of MG-OTNs by adopting multiple power levels at the MG nodes. Our proposed scheme combines the advantages of a previously proposed energy-efficient design method and introducing dual power state behavior to the MG nodes. According to the proposed scheme, a node can be either in the on state where it can add, drop and forward traffic at any granularity, or in the sleep state where it can only add and drop traffic, and accordingly, it saves the switching power consumption due to pass-through traffic. Through extensive simulations, we evaluate the proposed dual power state-based MG-OTN design in terms of energy-efficiency. Numerical results confirm that for all tested traffic patterns, putting a certain number of nodes in the sleep mode can guarantee significant power savings. Furthermore, we provide information on optimal selection of the nodes that have to be put in the sleep mode. Moreover, we study the topology dependence of the proposed scheme and show that high network connectivity leads to high power savings.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".