计算机科学
边缘计算
移动边缘计算
计算卸载
纳什均衡
甲骨文公司
分布式计算
计算机网络
GSM演进的增强数据速率
水准点(测量)
潜在博弈
回程(电信)
节点(物理)
博弈论
服务器
基站
数学优化
人工智能
大地测量学
数学
地理
微观经济学
经济
工程类
结构工程
软件工程
作者
Qing Li,Xiao Ma,Ao Zhou,Xiapu Luo,Fangchun Yang,Shangguang Wang
标识
DOI:10.1109/tmc.2021.3139362
摘要
In mobile edge computing networks, densely deployed access points are empowered with computation and storage capacities. This brings benefits of enlarged edge capacity, ultra-low latency, and reduced backhaul congestion. This paper concerns edge node grouping in mobile edge computing, where multiple edge nodes serve one end user cooperatively to enhance user experience. Most existing studies focus on centralized schemes that have to collect global information and thus induce high overhead. Although some recent studies propose efficient decentralized schemes, most of them did not consider the system uncertainty from both the wireless environment and other users. To tackle the aforementioned problems, we first formulate the edge node grouping problem as a game that is proved to be an exact potential game with a unique Nash equilibrium. Then, we propose a novel decentralized learning-based edge node grouping algorithm, which guides users to make decisions by learning from historical feedback. Furthermore, we investigate two extended scenarios by generalizing our computation model and communication model, respectively. We further prove that our algorithms converge to the Nash equilibrium with upper-bounded learning loss. Simulation results show that our mechanisms can achieve up to 96.99% of the oracle benchmark.
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