More Data, Less Energy Making Network Standby More Efficient in Billions of Connected Devices More Data, Less Energy Making Network Standby More Efficient in Billions of Connected Devices
Bibliographic record
Abstract
The electricity demand of our increasingly digital economies is growing at an alarming rate. While data centre energy demand has received much attention, of greater cause for concern is the growing energy demand of billions of networked devices such as smart phones, tablets and set-top boxes. In 2013, a relatively small portion of the world’s population relied on more than 14 billion of these devices to stay connected. That number could skyrocket to 500 billion by 2050, driving dramatic increases in both energy demand and wasted energy. Being connected 24/7 means these information and communication technology (ICT) devices draw energy all the time, even when in standby mode. This publication probes their hidden energy costs. In 2013, such devices consumed 616 terawatt hours (TWh) of electricity, surpassing the total electricity consumption of Canada. Studies show that for some devices, such as game consoles, up to 80 % of the energy consumption is used just to maintain a network connection. Implementing best available technologies could reduce the energy demand of network-enabled devices by up to 65%. In the absence of strong market drivers to optimise the energy performance of these devices, policy intervention is needed. Building on its experience in setting international policy for standby energy consumption of stand-alone devices, the International Energy Agency uses this publication to set the stage for tackling the much bigger challenge of network standby. In exploring both policy and technology solutions, the book charts a path forward and identifies which stakeholders should take the lead in particular areas. An underlying message is that there is a need for international cooperation across all parts of the ICT value chain. M ore D
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.017 |
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".