针对智慧城市的时延敏感型多依赖任务调度问题，提出了边缘计算赋能的智慧城市架构，并设计了一种计算迁移方法，以满足任务调度需求。首先建立了多依赖任务模型、任务的时延约束模型以及智慧城市服务器的负载约束模型。然后使用深度强化学习算法训练出可感知任务间依赖关系的智能体，以实时地进行计算迁移决策。一系列实验验证了该方法在时延、能耗优化方面的有效性。
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.
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