Distributed Task Offloading Optimization With Queueing Dynamics in Multiagent Mobile-Edge Computing Networks
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Bibliographic record
Abstract
Task offloading decision making plays a key role in enabling mobile-edge computing (MEC) technologies in Internet of Things (IoT). However, it meets the significant challenges arising from the stochastic dynamics of task queueing in the application layer and coupled wireless interference in the physical layer in a distributed multiagent network without any centralized communication and computing coordination. In this article, we investigate the distributed task offloading optimization problem with consideration of the upper layer queueing dynamics and the lower-layer coupled wireless interference. We first propose a new optimization model that aims at maximizing the expected offloading rate of multiple agents by optimizing their offloading thresholds. Then, we transform the problem into a game-theoretic formulation, which further leads to the design of a distributed best-response (DBR) iterative optimization framework. The existence of Nash equilibrium strategies in the game-theoretic model has been analyzed. For the individual optimization of each agent's threshold policy, we further propose a programming scheme by transforming a constrained threshold optimization into an unconstrained Lagrangian optimization (ULO). The individual ULO is integrated into the DBR framework to enable agents to cooperate and converge to a global optimum in a distributed manner. Finally, simulation results are provided to validate the proposed method and demonstrate its significant advantage over other existing distributed methods. The numerical results also show that the proposed method can achieve comparable performance to a centralized optimization method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.011 |
| 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