Economic and Energy Considerations for Resource Augmentation in Mobile Cloud Computing
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Bibliographic record
Abstract
In earlier works [1], [2], we proposed to utilize a centralized broker-node to perform task scheduling for the resource augmentation of a large number of mobile devices. The task scheduler model focused on energy optimization was proposed for the centralized task scheduling problem. In this paper, the model extends the optimization process by including an economic element to it. Thus, we propose an energy and monetary cost-aware mathematical task scheduler model. Compared to the previous model, this model, can allow mobile devices to offload multiple tasks to cloud resources. The results in this paper are more thorough and more aspects of task offloading have been analysed. For instance, the model is evaluated under two different resource augmentation environments for mobile cloud computing: a local private cloud and public clouds. More precisely, the task scheduling problem is optimally solved to minimize: (i) the total energy consumption when applied to a local private cloud, and (ii) the total energy consumption and monetary cost when applied to public clouds. Our proposed model at the centralized broker-node finds optimal solutions for task assignment problem, and provides a significant reduction in the total costs compared with the task assignment by the centralized scheduler without optimization.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it