Life cycle assessment of videoconferencing with call management servers relying on virtualization
Bibliographic record
Abstract
Recently, data centres have been called out for their particularly high energy consumption, which already accounts for 1.5% of the total global electricity consumption and is among the world's fastest growing energy consumptions.To reduce the data centres' environmental impacts, technologies such as free cooling and sustainable power sources are used.Another newly developed strategy to improve the energy efficiency of data centres is virtualization, which makes it possible to install several operating systems, known as virtual machines (VMs), so that several tasks and users can share a single server.To evaluate the environmental advantages and burdens of this strategy, assessments tools are required.Several studies have already quantified the energetic and environmental benefits of virtualization but often only considered the use phase and CO2 improvement.This study uses life cycle assessment (LCA) to evaluate the environmental impacts of Internet use in videoconferencing (VC).Preliminary results show the advantages of virtualization in the manufacturing, use and endof-life phases.Indeed, when virtualization is implemented, one server can be allocated to several tasks.Therefore, the environmental burden of use and manufacturing will be allocated to the various tasks, decreasing the impact of each one.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".