A Novel Cost Optimization Method for Mobile Cloud Computing by Capacity Planning of Green Data Center With Dynamic Pricing
Bibliographic record
Abstract
Due to the large volume of data, high processing time, and power consumption, operators are looking for ways to reduce the energy consumption and subsequently optimize the energy consumption of data centers. Appropriate pricing of services and control of user demands along with considering renewable energy in the data center lead to a reduction in energy consumption of both users and data centers. The proposed methods for simultaneous reduction in the cost of energy consumption and an increase in the number of processed demands in data centers are not very practical. This paper proposed the capacity planning with dynamic pricing algorithm considering different factors in energy consumption reduction in green data centers of the fourth/fifth generation of mobile system networks delivering mobile cloud computing services. The proposed algorithm determines the optimal number of servers and addresses the tradeoff between the cost of operation and the delay of services. A penalty function for cost was derived and various scenarios were designed and different qualities of services were considered using the Lyapunov optimization to set up the simulation environment. The provided results illustrate the efficiency of the proposed scheme and validate the mathematical model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".