Bibliographic record
Abstract
Datacenter Power Management in Smart Grids overviews recent work on managing and minimizing the cost of data centers in the context of smart grids. It starts by reviewing the operation of smart grids and analyzing how power is consumed in datacenters. Then, it presents various cost minimization approaches using techniques from the fields of optimization, algorithmics, and feedback control. In particular, it focuses on approaches that utilize time-of-use pricing and demand response features to cut the datacenter electricity cost. In a cloud computing environment, companies or individuals offload their computing to the cloud, which is supported by the computing infrastructure called datacenters. The operation of these datacenters consumes large amounts of electricity, bringing high costs and negatively impacting the environment. In the mean time, a new kind of electrical grid, the smart grid, is emerging. Smart grids enable two-way communications between the power generators and the power consumers. Smart grid technology brings many salient features to help deliver power efficiently and reliably. While a lot of research has been conducted on both datacenters and smart grids, Datacenter Power Management in Smart Grids takes the novel approach of considering both together and focuses on cost-aware datacenter power management in the presence of smart grids. This work reviews recent developments in this area and explains how a smart grid operates, where power goes in datacenters, and, most importantly, how to reduce the power cost and/or negative environmental impact when operating datacenters.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".