A MEC-based Distributed Offloading Model for Ubiquitous and Time-constraint Offloading
Bibliographic record
Abstract
The advancements in mobile hardware and network technologies facilitate the processing power, storage capability, and connection quality. Such developments enable sophistic functions, ubiquitous power- and bandwidth-hungry applications that fundamentally changes the individual's lifestyle. Although Cloud Computing technologies have already been leveraged to coordinate with the capability and battery-constraint mobile User Equipment (UE), the long-distance propagation delay downgrades the network QoS and user QoE. In this paper, we propose a queueing-based Mobile Edge Computing (MEC) model that concerns the offloading procedure, especially in the time-constraint scenarios. A queueing model is proposed for the offloading process, considering the dynamic network queueing delay. A heuristic scheduling model is designed to maximize the offloading energy and execution efficiency. A regression prediction model is implemented to achieve dynamic resource allocation. In the experiment, the proposed model is compared to the recent studies, and the results indicate that the proposed model can outperform the current studies in terms of execution time and energy reservation.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".