PLI-Aware Cost Management for Green Backbone All-Optical WDM Networks via Dynamic Topology Optimization
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Bibliographic record
Abstract
To cope with the energy inefficiency as well as the temporal uncertainty of real-world traffic in all-optical backbone networks, we explore the performance gains obtained from adaptively putting network elements into sleep mode, taking into account physical layer impairments (PLIs). Despite recent progress on link sleep mechanisms, the beneficial impacts of periodically activating and deactivating line amplifiers are seriously restricted by extra incurred operational expenditures due to accelerated aging of network equipment, which is a direct consequence of temperature fluctuations. In this paper, we revisit the problem of green PLI-constrained lightpath establishment, paying close attention to minimizing the number of on/off transitions. Toward this end, we formulate green lightpath establishment as a nonlinear multi-objective optimization problem, which addresses not only the energy efficiency, but also the grade of service and quality of service, using accurate models of a wide variety of linear/nonlinear PLIs. To tackle the developed problem under realistic scenarios, we propose the so-called green adaptive time-aware algorithm, which consists of lightpath establishment as well as wake-up/sleep procedures. The presented analysis followed by verifying simulations confirms that the proposed algorithm stands as a practical solution to the cost-efficient green impairment-constrained lightpath establishment problem under temporal uncertainly of incoming traffic.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it