Towards Energy Efficiency for Cloud Computing Services
Bibliographic record
Abstract
Over the past decade, the increasing complexity of data-intensive cloud computing services along with the exponential growth of their demands in terms of computational resources and communication bandwidth presented significant challenges to be addressed by the scientific research community. Relevant concerns have specifically arisen for the massive amount of energy necessary for operating, connecting, and maintaining the thousands of data centres supporting cloud computing services, as well as for their drastic impact on the environment in terms of increased carbon footprint. This chapter provides a survey of the most popular energy-conservation and “green” technologies that can be applied at data centre and network level in order to overcome these issues. After introducing the reader to the general problem of energy consumption in cloud computing services, the authors illustrate the state-of-the-art strategies for the development of energy-efficient data centres; specifically, they discuss principles and best practices for energy-efficient data centre design focusing on hardware, power supply specifications, and cooling infrastructure. The authors further consider the problem from the perspective of the network energy consumption, analysing several approaches achieving power efficiency for access, and core networks. Additionally, they provide an insight to recent development in energy-efficient virtual machine placement and dynamic load balancing. Finally, the authors conclude the chapter by providing the reader with a novel research work for the establishment of energy-efficient lightpaths in computational grids.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".