面向多目标状态感知的自适应云边协同调度研究

Study on Adaptive Cloud-Edge Collaborative Scheduling Methods for Multi-object State Perception

随着智能城市和工业智能制造的蓬勃发展,从监控摄像头获取详尽信息以进行多目标视觉分析的需求日益突出。现有研究主要关注在服务器上进行资源调度以及改良视觉模型,往往不能很好地应对设备资源状态和任务状态的动态变化。随着边缘端硬件资源的升级和任务处理模型的改进,设计一个自适应的云边协同调度模型来满足任务的实时用户需求成为优化多目标状态感知任务的重要方式。因此,在深入分析云边场景下多目标状态感知任务特性的基础上,提出了一种基于深度强化学习的自适应云边协同调度模型ATS-SAC。ATS-SAC通过实时解析多目标状态感知任务的运行时状态,动态给出任务执行的视频流配置、模型部署配置等调度决策,从而显著优化环境不稳定的云边场景下多目标状态感知任务的精度、时延的综合性能质量。同时,还引入了一种基于用户体验极限阈值的动作筛选方法,有助于去除冗余的决策动作,进一步优化模型的决策空间。针对用户对多目标状态感知任务性能结果的不同需求,ATS-SAC模型能提供包括极速模式、均衡模式和精度模式在内的多种灵活的调度策略。实验结果表明,相比其他的任务执行方式,在ATS-SAC模型的调度策略下,多目标状态感知任务在精度质量和处理时延上更能满足用户的体验需求。同时,当实时运行状态变化时,ATS-SAC模型能够动态调整其调度策略,以保持稳定的任务处理效果。

With the development of smart cities and intelligent industrial manufacturing,the demand for comprehensive information from surveillance cameras for multi-objective visual analysis has become increasingly prominent.Existing research mainly focuses on resource scheduling on servers and improvements of visual model,which often struggle to adequately handle dynamic changes in system resource and task state.With the advancement of edge hardware resources and task processing models,designing an adaptive cloud-edge collaborative scheduling model to meet the real-time user requirements of tasks has become an essential approach to optimize multi-objective state perception tasks.Thus,based on a profound analysis of characteristics of multi-objective state perception tasks in cloud-edge scenarios,this paper proposes a model of adaptive task scheduler based on soft actor-critic(ATS-SAC).ATS-SAC intelligently decides key factors of tasks such as video stream configuration and model deployment configuration according to real-time analysis of runtime state,thereby significantly optimizing the accuracy and delay of multi-objective state perception tasks in cloud-edge scenarios.Furthermore,we introduce an action filtering method based on user expe-rience threshold that helps to eliminate redundant decision-making actions,so as to reduce the decision-making space of the mo-del.Depending on user’s varied demands for performance outcomes of the multi-objective state perception tasks,ATS-SAC model can provide three flexible scheduling strategies,namely speed mode,balance mode,and precision mode.Experimental results show that,comparing to other executing methods,the scheduling strategies of ATS-SAC mo-del make multi-objective state perception tasks more satisfactory in terms of accuracy and delay.Moreover,when the real-time operating state changes,the ATS-SAC mo-del can dynamically adjust its scheduling strategies to maintain stable task processing results.