Cost-Effective Request Scheduling for Greening Cloud Data Centers
Bibliographic record
Abstract
With the popularity of cloud computing, many cloud service providers deploy regional data centers to offer services and pplications. These large-scale data centers have drawn extensive attention in terms of the huge energy demand and carbon emission. Thus, how to make use of their spatial diversities to green data centers and reduce cloud provider's costs is an important concern. In this paper, we integrate service reward, electricity cost, carbon taxes and service performance to study cost-effective request scheduling for cloud data centers. We propose an online and distributed scheduling algorithm CESA to chieve the flexible tradeoff between these conflicting objectives. The time complexity of CESA is polynomial, and it can be implemented in a parallel way. CESA requires no prior knowledge of the statistics of request arrivals or future electricity prices, yet it provably approximates the optimal system profit while bounding the queue length. Real-trace based simulations are conducted which verify the effectiveness of our CESA algorithm.
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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 | 0.001 | 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 | 0.002 |
| 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".