Bibliographic record
Abstract
Information and Communication Technology (ICT) has been estimated to consume 10% of the total energy consumption in industrial countries.According to the latest measurements, this amount is rapidly increasing by 6% annually.With the evolved new business model in which Service Providers (SPs) are separated from Infrastructure Providers (InPs), Virtualized Network Environments (VNEs) have been regarded as a promising technology for flexibly utilizing shared communication network resources.VNEs also play a fundamental role toward virtualizing data centers.In this thesis, we suggest different feasible solutions to optimize the energy consumption in a VNE.In this regard, first, we review the corresponding literature in regard to the architecture of a VNE, its performance modelling, several power models, and also existing energy-saving solutions for VNEs.We approach the objective of optimizing the energy consumption in a VNE by defining and solving two main problems.The first problem optimizes the energy consumption in a VNE during the off-peak period.This is feasible by reconfiguring the mapping of already embedded virtual networks for the off-peak time.This is planned in two smaller and simpler sub-problems with increasing the complexity and higher energy-saving levels.Our solutions enable the providers to adjust the level of the reconfiguration and accordingly control the possible traffic disruptions.In the second problem, we propose a novel energy-efficient embedding method that maps heterogeneous MapReduce-based virtual networks onto a heterogeneous data center physical network, energy-wise.We introduce a new incast problem that specifically may happen in Virtualized Data Centers (VDCs).The proposed embedding process also controls the incast queueing delay.A special thanks to my family.Words cannot express how grateful I am to my mother, father, and my sister, for all of the sacrifices that they have made on my behalf.Their prayer for me was what sustained me thus far.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".