A novel application model and an offloading mechanism for efficient mobile computing
Bibliographic record
Abstract
This paper presents a new application model and a novel execution path-based algorithm for offloading in Mobile Cloud Computing (MCC) environments. Application offloading is the mechanism by which parts or modules of the application are executed in cloud remote computational services. Such operation saves resources in the mobile device but also incurs costs of accessing the cloud and using the communication network connecting the mobile device and cloud. The offloading problem is to find an offloading decision that minimizes the total cost of executing the application. Previous solutions find a single offloading decision per module. However, the total cost of executing a module also depends on the sequence of module calls leading to its execution. We present a fine grained application model and a fast optimal offloading decision algorithm where multiple offloading decisions are made per module based on the execution paths leading to the module. We evaluate our solution for a face detection mobile applications in multiple network scenarios. We show that our model and algorithm offer offloading decisions that are significant faster than offloading decision by traditional offloading schemes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".