Green power analysis for Geographical Load Balancing based datacenters
Bibliographic record
Abstract
Variability and intermittency of green power is the main obstacle for its utilization. Different from other power consumption, due to the distributed nature, load balancing on geographical range can be used to dispatch computing tasks to the data centers with abundant renewable energy. The premise of this new strategy is: there is always abundant green power at some of the renewable power portfolio, yet this is not always the truth. The stable availability of renewable energy is built on the compensation of different power plants, but due to the constraint of constructed data centers and the on-site powering strategy, this compensation effect has not been fully explored. In this paper, we propose a solution for Renewable Energy Portfolio Optimization (REPO) problem, and take wind farm location selection as an example to stabilize the variable and intermittent wind power. The simulation is conducted based on the real-world climatic traces from 607 candidate wind farms. The optimal renewable energy portfolio can provide stable wind power supply at the price of 70 USD/MWh. When simulated with Google workload trace of May 2011, with installed capacity 4 times of average power demand, REPO can save 59.5% of energy while a combination (on Google data center locations) without consideration of mutual compensation could only save 30%.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".