A Survey on Green-Energy-Aware Power Management for Datacenters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Megawatt-scale datacenters have emerged to meet the increasing demand for IT applications and services. The hunger for power brings large electricity bills to datacenter operators and causes significant impacts to the environment. To reduce costs and environmental impacts, modern datacenters, such as those of Google and Apple, are beginning to integrate renewable or green energy sources into their power supply. This article investigates the green-energy-aware power management problem for these datacenters and surveys and classifies works that explicitly consider renewable energy and/or carbon emission. Our aim is to give a full view of this problem. Hence, we first provide some basic knowledge on datacenters (including datacenter components, power infrastructure, power load estimation, and energy sources' operations), the electrical grid (including dynamic pricing, power outages, and emission factor), and the carbon market (including cap-and-trade and carbon tax). Then, we categorize existing research works according to their basic approaches used, including workload scheduling, virtual machine management, and energy capacity planning. Each category's discussion includes the description of the shared core idea, qualitative analysis, and quantitative analysis among works of this category.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it