Bibliographic record
Abstract
The demand for cloud services is growing at a phenomenal rate, and so is the energy cost of the data centres powering those services. This is pressing cloud service providers to look for ways of reducing energy consumption. One approach is to utilize energy-efficient hardware and/or software in the data centres, the other approach is to relocate some services, e.g., personal files and data rendering, to end-host computers, a.k.a. peers. In the later approach, peers contribute their communication and computation resources to exchange data and provide services, while the data centre performs central administration and authentication, as well as backend processing. In this paper, we model the energy consumption for both approaches and then perform analytical studies. Our analysis shows that (1) making the data centre energy efficient can reduce the energy cost significantly; (2) the number of hops from the data centre to the peers and among peers directly influences the energy saving; (3) it is preferred to utilize peers that are already online for other purposes; (4) introducing content delivery network (CDN) servers and enabling proxy service on home modems are the keys to make a hybrid P2P-cloud network go green. We further verified these findings by simulation.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".