计算机应用 ›› 2019, Vol. 39 ›› Issue (5): 1523-1527.DOI: 10.11772/j.issn.1001-9081.2018081753

• 应用前沿、交叉与综合 • 上一篇    下一篇

移动边缘环境下面向工作流管理的计算迁移方法

伏舒存1,2, 付章杰1,2, 邢国稳1,2, 刘庆祥1,2, 许小龙1,2,3   

  1. 1. 南京信息工程大学 计算机与软件学院, 南京 210044;
    2. 南京信息工程大学 江苏省网络监控中心, 南京 210044;
    3. 南京大学 计算机软件新技术国家重点实验室, 南京 210023
  • 收稿日期:2018-08-23 修回日期:2018-11-04 发布日期:2019-05-14 出版日期:2019-05-10
  • 通讯作者: 许小龙
  • 作者简介:伏舒存(1994-),男,江苏淮安人,硕士研究生,主要研究方向:移动云计算、大数据;付章杰(1983-),男,河南南阳人,副教授,博士,CCF会员,主要研究方向:云计算、大数据安全;邢国稳(1975-),女,河北冀州人,副教授,博士研究生,主要研究方向:计算机网络、信息安全;刘庆祥(1998-),男,江苏徐州人,主要研究方向:云计算、大数据;许小龙(1988-),男,江苏海安人,讲师,博士,CCF会员,主要研究方向:云计算、绿色计算。
  • 基金资助:
    国家自然科学基金资助项目(61702277,61772283)。

Computation offloading method for workflow management in mobile edge computing

FU Shucun1,2, FU Zhangjie1,2, XING Guowen1,2, LIU Qingxiang1,2, XU Xiaolong1,2,3   

  1. 1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China;
    2. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China;
    3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing Jiangsu 210023, China
  • Received:2018-08-23 Revised:2018-11-04 Online:2019-05-14 Published:2019-05-10
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61702277, 61772283).

摘要: 针对移动边缘环境下移动设备大量的能源消耗问题,为了优化移动设备的能源消耗,提出一种能耗感知的工作流计算迁移(EOW)方法。首先,基于排队论分析边缘设备中计算任务的平均等待时间,建立了移动设备的时间模型和能耗模型;然后,基于非支配排序算法(NSGA-Ⅲ)提出对应的计算迁移方法,对工作流的计算任务进行合理的分配,将一部分计算任务留在移动设备处理,或者迁移到边缘计算平台和远程云端,实现每个移动设备的节能目标;最后,通过CloudSim仿真平台对提出的计算迁移方法进行仿真和对比实验。实验结果表明,EOW方法能够明显地减少每个移动设备的能源消耗,同时满足每一个工作流的截止时间的要求。

关键词: 能源消耗, 计算迁移, 边缘计算, 工作流, 截止时间

Abstract: The problem of high energy consumption for mobile devices in mobile edge computing is becoming increasingly prominent. In order to reduce the energy consumption of the mobile devices, an Energy-aware computation Offloading for Workflows (EOW) was proposed. Technically, the average waiting time of computing tasks in edge devices was analyzed based on queuing theory, and the time consumption and energy consumption models for mobile devices were established. Then a corresponding computation offloading method, by leveraging NSGA-Ⅲ (Non-dominated Sorting Genetic Algorithm Ⅲ) was designed to offload the computing tasks reasonably. Part computing tasks were processed by the mobile devices, or offloaded to the edge computing platform and the remote cloud, achieving the goal of energy-saving for all the mobile devices. Finally, comparison experiments were conducted on the CloudSim platform. The experimental results show that EOW can effectively reduce the energy consumption of all the mobile devices and satisfy the deadline of all the workflows.

Key words: energy consumption, computation offloading, edge computing, workflow, deadline

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