面向智慧城轨系统的云边协同计算架构设计与优化

Design and optimization of cloud-edge collaborative computing architecture for smart urban rail systems

摘要:

城市轨道交通是城市交通运力的主要承载系统之一，随着近年来智慧城市发展的需要越来越迫切，智慧城轨系统的设计、研究与优化也成为许多学者的研究方向和重心。智慧城轨系统要求列车具有智能算力，进而实现多种智能服务需求。由于列车的车载设备具有诸多限制，在其上部署高算力的计算机设备并不现实，故需引入其他设备为其提供算力支撑。本研究面向智慧城轨系统的特殊场景，基于5G和边缘智能，设计了一种面向智能任务的云边协同计算架构，并将该架构中资源分配的流程进行了数学建模，将其转化为最小化任务延迟的最优化问题。针对该最优化问题，本文采用离散随机逼近算法进行求解，以最小化智慧城轨系统的任务总处理延迟。仿真结果表明，该算法能够有效降低智慧城轨系统中的智能任务处理时延。

Abstract:

Urban rail transit is one of the primary systems for urban transportation capacity. With the increasing demand for the development of smart cities in recent years, the design, research, and optimization of smart rail transit systems have become the research direction and focus for many scholars. Smart rail transit systems require trains to have intelligent computing power to meet a variety of intelligent service needs. Due to the numerous limitations of on-board equipment in trains, it is not practical to deploy high-performance computing devices on them, hence the need to introduce other devices to provide computational support. This study, aimed at the special scenarios of smart rail transit systems, designs a cloud-edge collaborative computing architecture for intelligent tasks based on 5G and edge intelligence. The resource allocation process in this architecture is mathematically modeled and transformed into an optimization problem that minimizes task latency. To solve this optimization problem, this paper employs a discrete stochastic approximation algorithm to minimize the total processing delay of tasks in the smart rail transit system. Simulation results indicate that the algorithm can effectively reduce the processing delay of intelligent tasks in smart rail transit systems.