Greening the availability design of optical WDM networks
Bibliographic record
Abstract
Tremendously increasing bandwidth demands on the Internet require high transmission capacity and reliable end-to-end connections which are offered by optical WDM networks. Huge bandwidth demands of the applications cause rising energy consumption at the optical cross-connects which contributes a significant portion of the total electricity consumption. In this paper, we study the availability design of optical WDM networks in an energy-aware perspective. We propose Power-Aware Reliable Design (PARD) for optical networks which is mainly based on two-step multi-hop lightpath bypass concept aiming to provision survivable demands with minimized power consumption. Through simulations, we show that our proposed approach, PARD can guarantee high availability levels for the demands with a significant decrease in power consumption when compared to a lightpath non-bypass availability maximization design. Moreover, it is also shown that employment of PARD is more fair than a lightpath non-bypass availability maximization approach in terms of deviation of per-node power consumption. We also show that increasing the spare capacity at the backup virtual links causes further decrease in power consumption while leading to a slight decrease in connection availability. Furthermore, we present that migrating to greener resources further improves the CO2emissions of PARD significantly.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".