A Cloudlet-based Mobile Computing Model for Resource and Energy Efficient Offloading
Bibliographic record
Abstract
Due to the limitation of the mobile device battery lifespan, the concept of Mobile Cloud Computing(MCC) offloading is popularly introduced to handle the conflicts between the resource-hungry mobile tasks and the limited internal energy capacity. To further reduce the communication delay and energy cost between the offloader and offloadee, the Cloudlet model is proposed that performs as the agent of remote Cloud Datacenter. In this case, many offloading tasks used to be executed by the distant Cloud can be processed locally on the Cloudlet. However, unlike the remote Cloud that is assumed to provide ”unlimited” computing utility, the Cloudlet is still bounded regarding computing power, storage, network bandwidth and coverage. In this paper, a Cloudlet-based offloading model is proposed to enable energy and execution efficient offloading. A task-centric resource allocation model is presented to handle the resource limitation issue of the local Cloudlet. In the experiments, the proposed model is compared to the traditional device-based solutions, and the offloading execution results present an overall improvement of the offloading energy reservation as well as execution throughput.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".