ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (11): 2558-2570.doi: 10.7544/issn1000-1239.2021.20200621

• 高性能计算 • 上一篇    

边云协同计算中基于预测的资源部署与任务调度优化

苏命峰1,2,王国军3,李仁发4   

  1. 1(中南大学计算机学院 长沙 410083);2(湖南商务职业技术学院商务信息技术学院 长沙 410205);3(广州大学计算机科学与网络工程学院 广州 510006);4(湖南大学信息科学与工程学院 长沙 410082) (sumingfeng@csu.edu.cn)
  • 出版日期: 2021-11-01
  • 基金资助: 
    国家自然科学基金重点项目(61632009);湖南省自然科学基金项目(2019JJ70057);广东省自然科学基金项目(2017A030308006);国家重点研发计划项目(2020YFB1005804);中南大学中央高校基本科研业务费专项资金项目(2018zzts180)

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

Su Mingfeng1,2, Wang Guojun3, Li Renfa4   

  1. 1(School of Computer Science and Engineering, Central South University, Changsha 410083);2(School of Business Information Technology, Hunan Vocational College of Commerce, Changsha 410205);3(School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006);4(College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082)
  • Online: 2021-11-01
  • Supported by: 
    This work was supported by the Key Program of the National Natural Science Foundation of China (61632009), the Natural Science Foundation of Hunan Province (2019JJ70057), the Natural Science Foundation of Guangdong Province (2017A030308006), the National Key Research and Development Program of China (2020YFB1005804), and the Fundamental Research Funds for the Central Universities of Central South University (2018zzts180).

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.

关键词: 任务调度, 资源部署, 任务预测, 协同计算, 边缘计算

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).

Key words: task scheduling, resource deployment, task prediction, collaborative computing, edge computing

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