Power-aware design of the optical interconnect for future data centers
Bibliographic record
Abstract
The volume of traffic carried on the Optical Transport Network (OTN) is continuously growing, not only due to the increase of new services, new applications, and the number of both connected users and smart devices, but also owing to the rapidly growing Data Center Interconnect (DCI) market. As a result, telecom operators need to upgrade their transport networks to cope with these new traffic requirements. This tremendous increase in bandwidth demand will also introduce energy bottlenecks in OTNs. The telecommunication networks' significant Greenhouse Gas Emissions (GGE) is another challenge that must be addressed in OTN design and planning policies. Thus, energy-efficient OTN architectures are currently attracting the attention of telecom operators. The candidate OTN architectures are: (1) the conventional Dense Wavelength Division Multiplexing (DWDM) fixed grid with high Single Line Rates (SLRs); (2) the conventional DWDM fixed grid adopting Mixed Line Rates (MLRs); and (3) the Elastic Optical Network (EON) or flexigrid-based architecture which is enabled by the Optical Orthogonal Frequency Division Multiplexing (O-OFDM) technique. This paper evaluates the power consumption of the abovementioned OTN architectures under different traffic loads and patterns, as well as different network physical topologies. In order to perform the evaluation, new heuristic algorithms are specifically developed for the green design and planning of data center optical interconnects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".