Dynamic Power Management of Distributed Internet Data Centers in Smart Grid Environment
Bibliographic record
Abstract
The study of today's Cyber-Physical System (CPS) has been an important research area. Internet Data Centers (IDCs) are energy consuming CPSs that support the reliable operations of many important online services. Along with the increasing Internet services and cloud computing in recent years, the power usage associated with IDC operations had been surging significantly. Such mass power consumption has brought extremely heavy burden on IDC operators. Recently there are extensive research on power management for IDCs. While most work only consider about dynamical optimization of IDC under electricity markets, the reaction of IDC toward electricity market has been overlooked. Due to the fact that IDCs are usually large-volume users in the electricity market, they might have market power to affect the electricity price. In this paper, we study how to address the challenge of interactions between IDC operation and electricity market price. To this end, we propose a supply function to model the market power of IDC and formulate a total electricity cost minimization problem as a non-linear programming. In order to design efficient solution method, we transform the optimization problem to a quadratic programming. Extensive performance evaluations demonstrate that the proposed method can effectively minimize the total electricity cost of IDCs by adaptively handling the interaction between IDCs and smart grid.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".