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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".