A Two-Way Street: Green Big Data Processing for a Greener Smart Grid
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
Integrating renewables into the mainstream energy market is pivotal for the green revolution promised by the smart grid. The real power behind realization of the smart grid goals lies in the volume, variety, velocity of the big data generated by a variety of sources. Nevertheless, the smart grid needs data centers to digest the big data for its profound green revolution. However, big data processing is the radix for data centers to be seen as energy black holes. Unless data centers are transformed into energy-efficient enterprises, big data are going to be responsible for superfluous energy burn, potentially reversing the smart grid genesis with regard to green environmental impact. This paper describes the role of the big data enterprise in envisioning the smart grid. We dissect the big data enterprise into six vital planes impacting the energy footprints of data centers. We present a survey of key strategies to make these six vital planes greener. Moreover, we present open challenges and directions in this context. We assert that a cross-plane approach toward a greener optimization is crucial. In this vein, we present a green orchestrator that is capable of incorporating different planes in an integrated fashion to boost energy profile of the big data enterprise.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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