基于自组织劳动分工的边云协同任务调度与资源缓存算法  

Edge-cloud collaborative task scheduling and resource cache algorithm based on self-organizing division of labor

作　　者：赵璞 肖人彬[1] ZHAO Pu;XIAO Ren-bin(School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China;China North Vehicle Research Institute,Beijing 100072,China)

机构地区：[1]华中科技大学人工智能与自动化学院,武汉430074 [2]中国北方车辆研究所,北京100072

出　　处：《控制与决策》2023年第5期1352-1362,共11页Control and Decision

基　　金：科技创新2030——“新一代人工智能”重大项目(2018AAA0101200)。

摘　　要：针对边缘计算环境中,边缘设备的计算和存储资源有限的问题,探讨高效的边云协同任务调度和资源缓存策略,研究自组织劳动分工群智能算法模型机理,并以此为基础,提出基于蜂群劳动分工“激发-抑制”模型的边云协同任务调度算法(edge cloud collaborative task scheduling algorithm based on bee colony labor division‘activator-inhibitor’model,ECCTS-BCLDAI)和基于蚁群劳动分工“刺激-响应”模型的边云协同资源缓存算法(edge cloud collaborative resource caching algorithm based on ant colony labor division‘stimulus-response’model,ECCRC-ACLDSR).仿真实验结果表明:所提出的ECCTS-BCLDAI任务调度算法在降低平均任务执行时长、减少边云协同费用上相较于传统算法有更好的表现;所提出的ECCRC-ACLDSR资源缓存算法在降低任务平均时长、优化网络带宽占用率、减少边云协同费用上相较于传统算法更具有优越性.Aiming at the problem of limited computing and storage resources of edge devices in the edge computing environment,we discuss efficient edge-cloud collaborative task scheduling and resource caching strategies,and study the mechanism of the self-organizing labor division swarm intelligent algorithm model.On this account,the edge cloud collaborative task scheduling algorithm based on bee colony labor division‘activator-inhibitor’model(ECCTS-BCLDAI)and the edge cloud collaborative resource caching algorithm based on ant colony labor division‘stimulus-response’model(ECCRC-ACLDSR)are proposed.The simulation results show that the proposed task scheduling and resource caching algorithm have better performance than the traditional algorithm in reducing the average task execution time,optimizing network bandwidth usage and reducing the edge-cloud collaboration cost.

关 键 词：边缘计算 自组织劳动分工 任务调度 资源缓存 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]