GreenPlanning: Optimal Energy Source Selection and Capacity Planning for Green Datacenters
Bibliographic record
Abstract
Cloud service providers such as Microsoft and Google are beginning to power up their datacenters using multiple energy sources. To reduce cost and emission, they incorporate green energy sources into the power supply, while to improve service availability, they back up datacenters using traditional (usually brown) energy sources. However, challenge arises due to distinct characteristics of energy sources used for different goals. How to select optimal energy sources and plan their capacity for constructing datacenters to meet cost, emission and service availability requirement remains to be fully explored. This work provides a holistic solution to address this problem. We present GreenPlanning, a framework to strike a judicious balance among multiple energy sources, grid power and energy storage devices for a datacenter in terms of the above three goals. GreenPlanning investigates different features and operations of a wide spectrum of green and brown energy sources available to datacenters. The framework minimizes the lifetime total cost including both capital and operational cost for a datacenter. We conduct extensive simulations to evaluate GreenPlanning with real-life computational workload and meteorological data traces. Results demonstrate that GreenPlanning can reduce the lifetime total cost and emission by more than 50% compared to traditional configurations, while still satisfying service availability requirement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".