eco-IDC: Trade Delay for Energy Cost with Service Delay Guarantee for Internet Data Centers
Bibliographic record
Abstract
Cloud computing services are becoming integral part of people's daily life. These services are supported by Internet data centers (IDCs). As demand for cloud computing services soars, energy consumed by IDCs is skyrocketing. This paper studies an energy management problem - how to minimize energy cost for IDCs in deregulated electricity markets. While several existing works handle this problem by leveraging spatial diversity of electricity price, little has been done to address the temporal uncertainty in electricity price and arriving workload. This paper proposes a novel two-stage design and the eco-IDC (Energy Cost Optimization-IDC) algorithm to exploit temporal diversity of electricity price and dynamically schedule workload to execute on IDC servers through an input queue. Extensive evaluation experiments are performed to demonstrate that the proposed approach significantly reduces energy cost for IDCs, and guarantees a service delay bound for user requests.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 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".