A novel energy efficient platform based model to enable mobile Cloud applications
Bibliographic record
Abstract
Due to the nature of communication, mobility and portability in Mobile Computing, the handling of limited computing, storage and network capabilities become increasingly important especially when more features and richer functionality are required today. Cloud Computing, as an elastic computing utility provisioning framework, is shown to be a promising approach, addressing the concerns in Mobile Computing. Many achievements have been made by researchers regarding how to offload computational tasks from mobile systems to the Cloud. However, the proposed offloading methodologies are mainly from the perspectives of mobile application level, focusing on static estimation, dynamic partitioning, cloning, transmission overhead evaluation and migration. Issues related to multi-core Cloud systems are not fully considered, such as overall energy consumption of Cloud systems, information security, usability and availability. In this paper, a platform-based system model is designed from the view of the Cloud platform, trying to enable these Cloud benefits in addition to offloading, and to provide better execution efficiency and overall energy reduction by utilizing the proposed platform level scheduling. Based on the experiments, the proposed platform scheduling can achieve greater energy reduction with little computing overhead on the management node, compared to application-level scheduling methods.
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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 | 0.000 | 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 | 0.001 | 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 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".