Sustainable Offloading in Mobile Cloud Computing
Bibliographic record
Abstract
Mobile Cloud Computing (MCC) has been extensively explored to be applied as a vital tool to enhance the capabilities of mobile devices, increasing computing power, expanding storage capacity, and prolonging battery life. Offloading works as the fundamental feature that enables MCC to relieve task load and extend data storage through an accessible cloud resource pool. Several initiatives have drawn attention to delivering MCC-supported energy-oriented offloading as a method to cope with a lately steep increase in the number of rich mobile applications and the enduring limitations of battery technologies. However, MCC offloading relieves only the burden of energy consumption of mobile devices; performance concerns about Cloud resources, in most cases, are not considered when dynamically allocating them for dealing with mobile tasks. The application context of MCC, encompassing urban computing, aggravates the situation with very large-scale scenarios, posing as a challenge for achieving greener solutions in the scope of Cloud resources. Thus, this article gathers and analyzes recent energy-aware offloading protocols and architectures, as well as scheduling and balancing algorithms employed toward Cloud green computing. This survey provides a comparison among system architectures by identifying their most notable advantages and disadvantages. The existing enabling frameworks are categorized and compared based on the stage of the task offloading process and resource management types, describing current open challenges and future research directions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".