AGILE: A terminal energy efficient scheduling method in mobile cloud computing
Bibliographic record
Abstract
Abstract With the development of mobile telecommunication technology, mobile phones have become a necessary tool in daily life and provided us many conveniences. Meanwhile, the huge number of cell phones constitute a potential high performance data processing system, called mobile cloud computing, to strengthen capacity for individual devices. Many researchers have studied about the architectures and scheduling algorithms of mobile cloud computing. However, little work has been performed about how to schedule mobile application tasks in data centers to extend battery life for mobile terminals. To address this issue, we investigate agent models, mobile energy consumption models and data transmission models under different connection environments. Based on which, we propose a novel terminal energy efficient scheduling method (AGILE for short). AGILE compares energy consumption in cloud execution and mobile execution according to the actual wireless environment, then makes energy‐efficient decisions. Extensive experiments are conducted to evaluate the performance of the AGILE under different wireless channels, and the performance impact on different parameters are studied. The experimental results indicate that the proposed method can save mobile devices' energy effectively. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".