Dynamic Multi-user Computation Offloading for Mobile Edge Computing using Game Theory and Deep Reinforcement Learning
Bibliographic record
Abstract
Mobile edge computing (MEC) has appeared as a promising solution to fill the gap between the growing computationally intensive applications and limited computation capability of mobile devices by providing powerful computing services at the edge of the wireless access network. To use the services provided by the MEC more effectively, making efficient and reasonable offloading decisions is crucial. In this paper, we study the computation offloading of tasks from multiple users to a single-cell edge server under a dynamic environment. We consider a practical case wherein a group of mobile users with random mobility patterns use a common set of time-varying stochastic transmission channels to perform computation offloading, and the number of active users in the system randomly changes. To reduce the mutual interference among users when accessing the wireless channels, we adopt game theory to formulate the users’ computation offloading decision process as a stochastic game model. Next, we prove the existence of the Nash Equilibrium (NE) for the proposed game model by showing its equivalency to a weighted potential game which has at least one pure-strategy NE point. Then, we present distributed computation offloading algorithms by adopting a payoff-based multi-agent reinforcement learning (MARL) approach to reach the NE of the game. Finally, through simulation, we validate the effectiveness of the proposed algorithms by comparing them with the results obtained from other previously studied multi-agent learning algorithms as well as conventional Q-learning and deep Q-learning algorithms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".