Energy-Efficient Virtual Link Reconfiguration for Off-Peak Time
Bibliographic record
Abstract
Energy consumption in Information and Communication Technology (ICT) is a large portion of total energy consumed in industrial countries. Virtualized Network Environment (VNE) has recently emerged as a solution to address the challenges of future Internet. It is essential to develop novel techniques to reduce VNE's energy consumption. In this paper, we propose an energy saving method that optimizes VNE's energy consumption during the off-peak time. This method reconfigures mapping for some of the embedded virtual links in the off-peak period. The proposed strategy enables providers to adjust the level of the reconfiguration, and accordingly control probable traffic disruptions due to the reconfiguration. This problem is formulated as a Binary Integer Linear Program (BILP). Since the defined BILP is NP-hard, a novel heuristic algorithm is also suggested. The proposed energy saving methods are evaluated over random VNE scenarios. The results confirm the defined solutions are able to save notable amounts of energy during off- peak period, while still accommodating off-peak traffic demands of involved virtual networks.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".