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    苏命峰, 王国军, 李仁发. 边云协同计算中基于预测的资源部署与任务调度优化[J]. 计算机研究与发展, 2021, 58(11): 2558-2570. doi: 10.7544/issn1000-1239.2021.20200621
    引用本文: 苏命峰, 王国军, 李仁发. 边云协同计算中基于预测的资源部署与任务调度优化[J]. 计算机研究与发展, 2021, 58(11): 2558-2570. doi: 10.7544/issn1000-1239.2021.20200621
    Su Mingfeng, Wang Guojun, Li Renfa. Resource Deployment with Prediction and Task Scheduling Optimization in Edge Cloud Collaborative Computing[J]. Journal of Computer Research and Development, 2021, 58(11): 2558-2570. doi: 10.7544/issn1000-1239.2021.20200621
    Citation: Su Mingfeng, Wang Guojun, Li Renfa. Resource Deployment with Prediction and Task Scheduling Optimization in Edge Cloud Collaborative Computing[J]. Journal of Computer Research and Development, 2021, 58(11): 2558-2570. doi: 10.7544/issn1000-1239.2021.20200621

    边云协同计算中基于预测的资源部署与任务调度优化

    Resource Deployment with Prediction and Task Scheduling Optimization in Edge Cloud Collaborative Computing

    • 摘要: 数据集中处理的云计算模式提供交互迅速、绿色高效的多样化应用服务面临新挑战.将云计算能力扩展到边缘设备,提出了边云协同计算框架;设计了基于任务预测的资源部署算法,在云服务中心通过二维时间序列对任务进行预测,结合分类聚合、延迟阈值判定等优化边缘服务器任务运行所需资源部署;提出了基于帕累托优化的任务调度算法,在边缘服务器分2个阶段进行帕累托渐进比较得到用户服务质量和系统服务效应2个目标曲线的相切点或任一相交点以优化任务调度.实验结果表明:结合基于任务预测的资源部署算法与基于帕累托优化的任务调度算法在提高平均用户任务命中率基础上,其用户平均服务完成时间、系统整体服务效应度、总任务延迟率在不同用户任务规模、不同Zipf分布参数α的应用场景下,均优于基于帕累托优化的任务调度算法和基于FIFO(first input first output)的基准任务调度算法.

       

      Abstract: The cloud computing model of data centralized processing is facing new challenges for providing diversified application services with rapid interaction and green efficiency. In this paper, the cloud computing capability is extended to the edge devices, and an edge cloud collaborative computing framework is proposed. A resource deployment algorithm based on task prediction (RDTP) is designed. The tasks are predicted by two-dimensional time series in cloud service center, and the task resource deployment of edge server is optimized by classification aggregation and delay threshold determination. A task scheduling algorithm based on Pareto improvement (TSPI) is proposed. At the edge servers, the Pareto progressive comparison is conducted in two stages to obtain the tangent point or any intersection point of the two objective curves of quality of user service and effect of system service to optimize task scheduling. The experimental results show that combining the resource deployment algorithm based on task prediction and the task scheduling algorithm based on Pareto improvement (RDTP-TSPI) increases the average user task hit rate. In addition, in the application scenarios of varying user task scales and different Zipf distribution parameters α, the average service completion time of users, the overall service effectiveness of system, and the total task delay rate of RDTP-TSPI are better than the TSPI and BA (benchmark task scheduling algorithm based on FIFO).

       

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