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Title	Computing Offloading Of Multi-Dependent Tasks In Smart Cities; [面向智慧城市的多依赖任务计算迁移研究]
ID_Doc	15454
Authors	Peng K.; Liu P.; Xu X.; Zhou X.
Year	2023
Published	Yingyong Kexue Xuebao/Journal of Applied Sciences, 41, 3
DOI	http://dx.doi.org/10.3969/j.issn.0255-8297.2023.03.003
Abstract	Aiming at the delay-sensitive multi-dependent task scheduling problem of smart cities, this paper proposes a smart city architecture empowered by edge computing and designs a computation offloading method to meet the scheduling requirements of tasks. Firstly, this paper first establishes a multi-dependent task model, as well as a latency constraint for the task and a load balancing constraint model for the smart city server. Secondly, agents that perceive dependencies between tasks are trained using deep reinforcement learning algorithms to make computational transfer decisions in real-time. Finally, a series of experiments are conducted to verify the effectiveness of this method in latency and energy consumption optimization. © 2023 Press of Shanghai Scientific and Technical Publishers. All rights reserved.
Author Keywords	computation offloading; deep reinforcement learning; latency-sensitive task; multi-dependency task; smart cities

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