Bibliographic record
Abstract
The demand for multimedia streaming is growing at a phenomenal rate, and data-centre-empowered streaming services are becoming more common nowadays. To better accommodate the demand, the computing power and networking components in data centres are being upgraded regularly, leading to higher energy bills. Many studies have been conducted around reducing the power consumed by data centres, but very little attention has been paid to the data transmissions and the power consumed by the entire system, from the streaming source to the end-user devices. In this paper, we are interested in the power efficiency of multimedia streaming system as a whole. We take on the analysis in three directions: traffic imposed by different streaming protocols, underlay physical network structure, and the network infrastructure (data centre vs. Peer-to-Peer). Three main conclusions drawn from the study are: (1) very insignificant power savings can be achieved by tuning the streaming protocol, router connectivity, or the network infrastructure, (2) significant power savings can be achieved through reducing the idle power of routers and end-user devices, and (3) the energy efficiency of the end-user devices determines whether the Peer-to-Peer (P2P) infrastructure can be a green alternative for data-centre-empowered streaming.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".