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Lookup NU author(s): Yizhou Shen
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Smart devices (SDs) used in the Industrial Internet of Things can generate computational tasks for processing the data generated during production. However, due to the limited processing power of SDs, it is necessary to transfer these computational tasks to more powerful devices for processing. To this end, we propose a Mobile Edge Computing (MEC) system based on a Software Defined Network (SDN) for SDs to offload their computational tasks. This MEC system includes multiple MEC servers to handle numerous SDs, which leads to load-balancing challenges among these servers. To tackle this problem, we develop a computational offloading model based on mean-field game theory and introduce a mean-field game-based load-balancing algorithm (MFGLB), which reduces processing latency and facilitates task scheduling through Multi-Agent Deep Reinforcement Learning. Each SD in the MEC system is considered a participant in the mean-field game, simplifying the complex stochastic game into a more manageable dual-agent game. We then prove the existence of Nash Equilibrium for this mean-field game. To evaluate the effectiveness of our MFGLB algorithm, we compare its performance with traditional load-balancing algorithms and a stochastic game-based load-balancing algorithm. Our experimental results demonstrate the superiority of MFGLB in reducing processing latency and addressing load imbalances.
Author(s): Wu G, Wang H, Zhang H, Shen Y, Shen S, Yu S
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Mobile Computing
Year: 2024
Pages: ePub ahead of print
Online publication date: 02/08/2024
Acceptance date: 02/04/2018
ISSN (print): 1536-1233
ISSN (electronic): 1558-0660
Publisher: IEEE
URL: https://doi.org/10.1109/TMC.2024.3437761
DOI: 10.1109/TMC.2024.3437761
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