分布式边缘计算系统的QoS优化:一种基于多智能体状态的学习方法

Fenghui Zhang, M. Wang, Liqing Shan, Xiangqing Wang, Maosheng Fu, Xiancun Zhou
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引用次数: 0

摘要

将边缘计算服务器放置在网络的边缘可以减少任务传输延迟。将它们连接成一个系统可以提供更广泛的服务。但是,由于人群和移动设备的移动性,每个边缘服务器卸载的任务数量可能相差很大,这将严重影响系统的QoS。为此,我们从博弈论的角度研究了分布式边缘计算系统的QoS改进,并提出了一种基于多智能体状态的学习算法。首先,通过将边缘计算服务器的成本建模为其执行时间与系统平均执行时间之间的偏差,我们将系统的QoS改进制定为基于状态的博弈,其中每个代理都竞争以最大化自己的效用。然后,我们提出了一种基于多智能体状态的学习算法,以获得每个智能体的纯纳什均衡策略。最后,通过与现有方法的比较,实验表明本文算法能够提高分布式边缘计算系统的QoS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
QoS Optimization for Distributed Edge Computing System: A Multi-agent State-based Learning Approach
Placement of edge computing servers at the edge of the network can reduce task transmission delay. Connecting them into a system can provide services for a wider range. However, due to the mobility of the crowd and mobile devices, the number of tasks offloaded to each edge server may be quite different, which will seriously affect the QoS of the system. To this end, we investigate the QoS improvement of the distributed edge computing system from the game-theoretic perspective and propose a multi-agent state-based learning algorithm. Firstly, by modeling the cost of an edge computing server as the deviation between its execution time and the system average execution time, we formulate the QoS improvement of the system as a state-based game where each agent competes to maximize its own utility. Then, we propose a multi-agent state-based learning algorithm to obtain the pure Nash equilibrium strategy of each agent. Finally, compared with the existing approaches, the experiments show that the proposed algorithm can improve the QoS of the distributed edge computing system.
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