基于深度强化学习的无人机辅助边缘计算延迟感知任务卸载

Mingxuan Huang, Kaixuan Sun, Yunpeng Hou, Zhicheng Ye, Yuanlong Wan, Huasen He
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引用次数: 0

摘要

多址边缘计算以其出色的计算能力和快速的交互能力被广泛应用于各种物联网设备中。如何提高边缘计算的业务可扩展性,优化计算卸载策略,成为提高边缘计算用户服务质量的关键。然而,传统的基于数学规划的卸载策略在动态场景下暴露出固有的局限性,无法满足多移动终端大面积分布的需求。为此,本文利用无人机建立多无人机辅助边缘计算框架,扩展服务范围,并提出一种基于强化学习的卸载策略,将不断增长的计算需求从移动终端卸载到边缘服务器。通过将移动终端和无人机的状态映射到相应的动作空间,然后将计算任务卸载给无人机,可以有效降低移动终端的计算和处理任务所带来的能耗。综合考虑状态空间潜在的维数灾难和设备数量增加带来的收敛失效,提出了一种基于深度强化学习的计算卸载策略。此外,我们还设计了负载均衡机制,以提高无人机的处理能力。实验结果表明,该算法能够有效降低移动终端的计算能耗,避免任务超时,收敛时间短。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep reinforcement learning based delay-aware task offloading for UAV-assisted edge computing
Multi-access edge computing has been widely used in various Internet of Things (IoT) devices because of its excellent computing and fast interaction abilities. How to improve the service extensibility of edge computing and optimize the computing offload strategy has become the key to improve the quality of service to edge computing users. However, the traditional offloading strategy based on mathematical programming has exposed its inherent limitations in dynamic scenarios, and cannot meet the requirements of multiple-mobile terminals distributed in a large area. Therefore, this paper use Unmanned Aerial Vehicles (UAVs) to establish a multi-UAV-assisted edge computing framework for extending the service range, and proposes an offloading strategy based on reinforcement learning to offload the growing computing requirements from mobile terminals to edge servers. By mapping the states of mobile terminals and UAVs to the corresponding action space, and then offloading computing tasks to UAVs, the energy consumption caused by computing and processing tasks of mobile terminals can be effectively reduced. Jointly considering the potential dimensional disaster of state space and the convergence failure imposed by the increase of device numbers, a novel computation offloading strategy based on deep reinforcement learning is proposed. Moreover, we design a load-balancing mechanism in the UAVs to improve the processing capacity. Experimental results prove that our proposed algorithm can effectively reduce the computing energy consumption of mobile terminals and avoid task timeout with a short convergence time.
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