首页> 外文期刊>Future generation computer systems >A computation offloading method over big data for IoT-enabled cloud-edge computing
【24h】

A computation offloading method over big data for IoT-enabled cloud-edge computing

机译:大数据的计算分流方法,用于支持物联网的云边缘计算

获取原文
获取原文并翻译 | 示例

摘要

The Internet of mobile things is a burgeoning technique that generates, stores and processes big real-time data to render rich services for mobile users. In order to mitigate conflicts between the resource limitation of mobile devices and users' demands of decreasing processing latency as well as prolonging battery life, it spurs a popular wave of offloading mobile applications for execution to centralized and decentralized data centers, such as cloud and edge servers. Due to the complexity and difference of mobile big data, arbitrarily offloading the mobile applications poses a remarkable challenge to optimizing the execution time and the energy consumption for mobile devices, despite the improved performance of Internet of Things (IoT) in cloud-edge computing. To address this challenge, we propose a computation offloading method, named COM, for IoT-enabled cloud-edge computing. Specifically, a system model is investigated, including the execution time and energy consumption for mobile devices. Then dynamic schedules of data/control-constrained computing tasks are confirmed. In addition, NSGA-III (non-dominated sorting genetic algorithm III) is employed to address the multi-objective optimization problem of task offloading in cloud-edge computing. Finally, systematic experiments and comprehensive simulations are conducted to corroborate the efficiency of our proposed method. (C) 2019 Elsevier B.V. All rights reserved.
机译:移动物联网是一种新兴技术,可以生成,存储和处理大型实时数据,以为移动用户提供丰富的服务。为了减轻移动设备的资源限制与用户减少处理延迟以及延长电池寿命的需求之间的冲突,它催生了一种将移动应用程序卸载以执行到集中式和分散式数据中心(例如云和边缘)的流行浪潮服务器。由于移动大数据的复杂性和差异性,尽管物联网(IoT)在云边缘计算中的性能得到了改善,但任意卸载移动应用程序对优化执行时间和移动设备的能耗提出了巨大挑战。为了应对这一挑战,我们提出了一种名为COM的计算分流方法,用于支持IoT的云边缘计算。具体来说,研究了一个系统模型,其中包括移动设备的执行时间和能耗。然后,确定数据/控制受限的计算任务的动态时间表。此外,采用NSGA-III(非支配排序遗传算法III)来解决云边缘计算中任务卸载的多目标优化问题。最后,进行了系统的实验和综合的仿真,以证实我们提出的方法的效率。 (C)2019 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Future generation computer systems》 |2019年第6期|522-533|共12页
  • 作者单位

    Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing, Jiangsu, Peoples R China|Nanjing Univ Informat Sci & Technol, Jiangsu Engn Ctr Network Monitoring, Nanjing, Jiangsu, Peoples R China|Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Jiangsu, Peoples R China;

    Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Comp Sci & Technol, Nanjing, Jiangsu, Peoples R China|Nanjing Univ Informat Sci & Technol, Jiangsu Engn Ctr Network Monitoring, Nanjing, Jiangsu, Peoples R China;

    Guizhou Univ, Fac Comp Sci & Technol, Guiyang, Guizhou, Peoples R China;

    Huaqiao Univ, Engn Inst, Quanzhou, Peoples R China;

    Univ Auckland, Dept Elect & Comp Engn, Auckland, New Zealand;

    Nanjing Univ Sci & Technol, Dept Comp Sci & Technol, Nanjing, Jiangsu, Peoples R China;

    Qufu Normal Univ, Sch Informat Sci & Engn, Chinese Acad Educ Big Data, Jining, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    IoT; Big data; Cloud-edge computing; Computation offloading; Energy consumption;

    机译:物联网;大数据;云计算;计算分流;能耗;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号