Off-Peak Energy Optimization for Links in Virtualized Network Environment
Why this work is in the frame
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Bibliographic record
Abstract
Energy consumption in information and communication technology (ICT) is estimated to be 10 percent of the total energy consumed in industrial countries. Besides, the population of ICT customers is growing. In order to handle the increasing traffic demands, service providers need to expand their network infrastructure. The recent proposed network virtualization technology helps slow down the infrastructure expansion by allowing the coexistence of multiple virtual networks over a single physical network. Although virtualized network environment (VNE) slows down the infrastructure expansion and therefore controls power consumption, it is essential to develop new techniques to decrease VNE's energy consumption. In this paper, we discuss multiple novel energy saving reconfiguration methods that globally/locally optimize VNE's link power consumption, during off-peak time. The proposed fine-grained local reconfiguration enables the providers to adjust level of the reconfiguration, and accordingly control possible traffic disruptions. An Integer Linear Program (ILP) is formulated for each solution according to two power models, and considering the impact of traffic splitability. Because the formulated ILPs are not scalable to large network sizes, a novel heuristic algorithm is also suggested. The simulation results prove the proposed solutions are able to save notable amount of energy in physical links during off-peak time.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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 it