云边环境下按需分配物理资源的任务卸载策略

The Mobile Task Offloading Strategy for Allocating Physical Resources on Demand in Dynamic Cloud-edge Environment

针对有限边缘服务器资源最大限度满足众多移动任务有着截止时间要求的移动任务卸载问题,提出了一种云边协同卸载移动任务的模型。该模型首先分析了影响移动任务服务需求和虚拟机服务保障的因素并给出度量方法,以及移动任务与虚拟机的服务匹配度的度量方法。其次,设计了一种动态云边环境下按需分配物理资源的移动任务卸载策略,该策略基于改进匈牙利算法求一批任务的最大化服务匹配度的卸载方案,并通过有限次迭代消除资源竞争进一步优化卸载方案。最后,将所提算法与P2PITS、ALBOA和ESSDSA算法进行对比,结果表明:相对于P2PITS算法,所提算法虚拟机负载率降低了30.1%,平均等待时间降低了13%;相对于ALBOA算法,所提算法平均完成时间降低了38.6%;相对于ESSDSA算法,所提算法执行成功率提高了3.5%。所提算法能够在满足用户截止时间要求下有效提高资源利用率,降低任务的平均完成时间。

Aiming at the unloading problem of mobile tasks for the limited edge server resources to maximize the satisfaction of numerous mobile tasks with deadline requirements,a model for cloud-edge-device collaboration was proposed to offload mobile tasks.Firstly,the model analyze the factors that affect the service demand of mobile tasks and the service guarantee of virtual machines,and give the measurement method,as well as the measurement method of the service matching degree between mobile tasks and virtual machines.Secondly,a mobile task offloading strategy was designed for on-demand allocation of physical resources in a dynamic cloud-edge environment.Based on the improved Hungarian algorithm,the purpose of this strategy was to find an offloading plan that could maximize service matching for a batch of tasks,and to further optimize the offloading plan by eliminating resource competition through a limited number of iterations.Finally,the algorithm in this study was compared with the P2PITS algorithm,the ALBOA algorithm and the ESSDSA algorithm from many aspects.Experimental results showed that compared with the P2PITS algorithm,the algorithm in this study reduced the virtual machine load rate by 30.1%,the average waiting time by 13%,compared with the ALBOA algorithm,the algorithm in this study reduce the average completion time by 38.6%on average,compared with the ESSDSA algorithm,the algorithm in this study increased the execution success rate by 3.5%on average.The proposed algorithm could effectively improve resource utilization and reduce the average completion time of tasks while meeting user deadline requirements.