In recent years, the number of devices and terminals connected to the smart city has increased significantly. Edge networks face a greater variety of connected objects and massive services. Considering that a large number of services have different QoS requirements, it has always been a huge challenge for smart city to optimally allocate limited computing resources to all services to obtain satisfactory performance. In particular, delay is intolerable for services in certain applications, such as medical, industrial applications, etc, that such applications require the high priority. Therefore, through flexibly dynamic scheduling, it is crucial to schedule services to the optimal node to ensure user experience. In this paper, we propose a resource allocation scheme for hierarchical edge computing network in smart city based on attention mechanism, for extracting a small number of features that can represent services from a large amount of information collected from edge nodes. The attention mechanism is used to quickly determine the priority of the services. Based on this, task deployment and resource allocation for different task priorities are developed to ensure the quality of service in smart cities by introducing Q-learning. Simulation results show that the proposed scheme can effectively improve the edge network resource utilization, reduce the average delay of task processing, and effectively guarantee the quality of service.

近年来，接入智慧城市的设备和终端数量大幅增加。边缘网络面临着更加多样化的连接对象和海量服务。考虑到大量服务具有不同的QoS要求，如何将有限的计算资源优化分配给所有服务以获得满意的性能一直是智慧城市面临的巨大挑战。特别是某些应用中的服务延迟是难以忍受的，例如医疗、工业应用等，这些应用需要较高的优先级。因此，通过灵活的动态调度，将业务调度到最优节点，保证用户体验至关重要。在本文中，我们提出了一种基于注意力机制的智慧城市分层边缘计算网络的资源分配方案，用于从边缘节点收集的大量信息中提取少量可以代表服务的特征。注意力机制用于快速确定服务的优先级。在此基础上，通过引入Q-learning，制定不同任务优先级的任务部署和资源分配，以保证智慧城市的服务质量。仿真结果表明，该方案能够有效提高边缘网络资源利用率，降低任务处理的平均延迟，有效保证服务质量。