Multi-Persona Mobility: Joint Cost-Effective and Resource-Aware Mobile-Edge Computation Offloading
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multi-persona mobile computing has begun to make its way to determine the battle about practical strategy for adopting personal devices in workplace. Though its competency, multi-persona performance and viability are critically threatened by the limited resources of mobile devices. In recent years, mobile edge computing (MEC) has risen as promising paradigm within the internet of things era bringing benefits to the proximity of mobile terminals, leveraging intelligent computations offloading services to address the severity of their resource scarcity. Yet, embracing mobile edge-based services to augment personas resources and performance raises new concerns including determining what computations to offload for serving the highest number of mobile devices and reducing the remote execution fees imposed on the institution. In this context, we propose new cost-effective MEC-based solution to address these issues. We develop two-level multi-objective optimization realized through an intelligent offloading decision model able to settle both concerns, by minimizing processing, memory and energy while augmenting virtual mobile instances performance on a wide range of physical devices with minimal offloading service fees. We also propose a redesigned smart genetic-based method able to accelerate and reduce the overhead of offloading decision evaluation. Extensive analysis is performed and the results show that our proposition can get more quickly the offloading strategy than other schemes. The results also demonstrate the ability to enforce the virtual mobile devices by reducing local processing, memory usage, energy consumption and execution time along with acceptable minimal additional fees compared to other techniques.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it