Distributed Multiuser Computation Offloading for Cloudlet-Based Mobile Cloud Computing: A Game-Theoretic Machine Learning Approach
Bibliographic record
Abstract
In this paper, we investigate the problem of multiuser computation offloading for cloudlet-based mobile cloud computing in a multichannel wireless contention environment. The studied system is fully distributed so that each mobile device user can make the offloading decisions based only on its individual information, and without information exchange. We first formulate this multiuser computation offloading decision making problem as a noncooperative game. After analyzing the structural property of the formulated game, we show that it is an exact potential game, and has at least one pure-strategy Nash equilibrium point (NEP). To achieve the NEPs in a fully distributed environment, we propose a fully distributed computation offloading (FDCO) algorithm based on machine learning technology. We then theoretically analyze the performance of the proposed FDCO algorithm in terms of the number of beneficial cloudlet computing mobile devices and the system-wide execution cost. Finally, simulation results validate the effectiveness of our proposed algorithm compared with counterparts.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".