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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".