Off-Peak Energy Optimization for Links in Virtualized Network Environment
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".