Overlay energy circle formation for cloud data centers with renewable energy futures contracts
Bibliographic record
Abstract
Cloud data centers are significant players in the electricity market due to their high energy consumption. It is highly desired to operate cloud data centers on only renewable energy such as solar, wind or tidal to reduce their cost and emissions. In the literature, utilization of renewable energy is maximized through workload migration towards data centers that are forecasted to have surplus renewable energy which is also known as “follow the sun chase the wind” approach. In practice, it is rather difficult and unrealistic to shift workloads based on instantaneous output of renewable generation. Generally forecast tools are used which can only provide a rough estimate of the generation capacity. In this paper, we use a more realistic approach and propose an overlay architecture to form energy circles of cloud data centers depending on their load and renewable energy futures contracts. A futures contract is an electricity purchase agreement between the data center operator and the renewable energy generator to purchase electricity in the future with today's price. Futures contracts are electricity market mechanisms that reduce the cost related risks for both parties and are seen as tools to scale-up renewable generation. On the other hand, fluctuating loads of cloud data centers may leave some contract capacity left unused or exceed the capacity. In this case, electricity will be purchased at current market price from the renewable generator or the utility grid where in both cases the electricity bill will increase. Hence, a mechanism to utilize the unused capacity in the contracts of peer data centers and shifting workloads towards available capacity can reduce bills as well as increase the utilization of renewable energy. Our proposed energy circles approach aims to group cloud data centers to achieve those goals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".