基于工业边缘计算的缓存任务调度方法

Caching Task Scheduling Method Based on Industrial Edge Computing

随着工业物联网(IIoT)的发展以及工业4.0浪潮的到来,工业边缘计算领域也得到了越来越多的关注。然而,依赖于工业云的传统工业计算方案让数据时延、数据安全面临着更大的挑战。本文针对工业边缘计算场景中遇到的多计算任务与单计算资源受限之间的矛盾,基于多台边缘计算控制器(AECC)的部署,进行订单下发场景设计,对串行队列任务进行编排与子队列分配,完成任务优先级排列,提高了业务服务价值,并结合缓存机制在约束条件下,完成了任务计算时间优化,降低了设备能耗,得到了一种贴合工业任务订单场景的基于轮询时间的计算任务卸载优化方法。通过仿真,本文方法在优化目标上得到了理想的效果。

With the development of the Industrial Internet of Things(IIoT)and the arrival of the Industry 4.0 wave,the field of industrial edge computing has received increasing attention.However,it has posed greater challenges to the data latency and data security due to the traditional industrial computing solutions that rely on industrial cloud.This paper aims at the contradiction between multi computing tasks and insufficient single computing resources in IIoT scenarios.Meanwhile,based on the deployment of the Edge Computing Controller(ECC),the order issuance scenario are designed to arrange the serial queue tasks and allocates sub queues,which is combined with caching mechanism to increase business service value,and finally reach the optimization for the calculation completion time of tasks to reduce the energy consumption of the equipment.Ultimately,a time-based calculation task offloading optimization method that fits the industrial task order scenario was obtained,and even an ideal effect is achieved on the optimization target through simulation.