Green Packet Optical Transport Networks (P-OTNs) Based on Photonic PBB-TE Switches and Minimized EEE Overhead
Bibliographic record
Abstract
Carrier Ethernet exhibits an enormous potential to be a cost-effective and less complex replacement of SONET/SDH especially after the ratification of IEEE standard 802.1Qay Provider Backbone Bridge-Traffic Engineering (PBB-TE). The recent IEEE standard 802.3az Energy Efficient Ethernet (EEE) presents another opportunity for service providers to select Ethernet as a technology of choice in the backbone while leveraging on its promise for achieving green transport network. In this paper, we propose two novel architectures of photonic PBB-TE core and edge switches, which enhance the usability of PBB-TE networks by reducing power consumption in individual switches in conjunction with passive optical bypassing and EEE. We also formulate the problem of energy-aware scheduling as an optimization problem whose objective is to minimize the overall energy consumption for transmitting Ethernet frames while satisfying their delay requirements. This model will be used as a benchmark while evaluating the performance of packet coalescing, a promising and recently proposed approach, as well as the performance of EEE.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".