《计算机应用》唯一官方网站 ›› 2020, Vol. 40 ›› Issue (2): 328-334.DOI: 10.11772/j.issn.1001-9081.2019081367

• 2019年全国开放式分布与并行计算学术年会(DPCS 2019)论文 • 上一篇    下一篇

基于多微云协作的计算任务卸载

王庆永, 毛莺池(), 王绎超, 王龙宝   

  1. 河海大学 计算机与信息学院,南京 211100
  • 收稿日期:2019-07-31 修回日期:2019-08-20 接受日期:2019-09-17 发布日期:2019-09-29 出版日期:2020-02-10
  • 通讯作者: 毛莺池
  • 作者简介:王庆永(1995—),男,河南固始人,硕士研究生,CCF会员,主要研究方向:云计算、边缘计算
    王绎超(1994—),男,山西介休人,硕士研究生,CCF会员,主要研究方向:云计算、边缘计算
    王龙宝(1977—),男,江苏盐城人,讲师,博士,CCF会员,主要研究方向:大数据、云计算、管理信息化。
  • 基金资助:
    国家重点研发计划项目(2018YFC0407905);华能集团重点研发课题资助项目(HNKJ17-21)

Computing task offloading based on multi-cloudlet collaboration

Qingyong WANG, Yingchi MAO(), Yichao WANG, Longbao WANG   

  1. College of Computer and Information,Hohai University,Nanjing Jiangsu 211100,China
  • Received:2019-07-31 Revised:2019-08-20 Accepted:2019-09-17 Online:2019-09-29 Published:2020-02-10
  • Contact: Yingchi MAO
  • About author:WANG Qingyong, born in 1995, M. S. candidate. His research interests include cloud computing, edge computing.
    WANG Yichao, born in 1994, M. S. candidate. His research interests include cloud computing, edge computing.
    WANG Longbao, born in 1977, Ph. D., lecturer. His research interests include big data, cloud computing, management informationization.
  • Supported by:
    the National Key Research and Development Program of China(2018YFC0407905);the Key Research and Development Project of China Huaneng Group(HNKJ17-21)

摘要:

针对多微云计算模式下计算任务卸载过程复杂、任务响应时间长的问题,构建面向多微云协作的计算任务卸载模型,并提出加权自适应惯性权重的粒子群优化(WAIW-PSO)算法,快速求解最优卸载策略。首先,对移动终端-微云-远程云的任务执行过程进行建模;其次,考虑多用户对计算资源的竞争,构建基于多微云协作的任务卸载模型;最后,针对求解最佳任务卸载策略复杂度过高的情况,提出WAIW-PSO算法求解卸载问题。仿真实验结果表明,与标准粒子群优化(PSO)算法以及基于高斯函数递减惯性权重的粒子群优化(GDIWPSO)算法相比,WAIW-PSO算法可以根据进化代数和个体适应度综合调整惯性权重,寻优能力较强,求解最优卸载策略的时间最短;在不同设备数、任务数等情况下选择不同任务卸载策略进行对比实验的结果表明,基于WAIW-PSO算法的卸载策略可以明显缩短任务总完成时间。

关键词: 移动云计算, 微云, 任务卸载, 多微云协作, 粒子群优化

Abstract:

Focusing on the problems of complex process and long response time of task offloading in multi-cloudlet mode, a computing task offloading model based on multi-cloudlet collaboration was constructed, and a Weighted self-Adaptive Inertia Weight Particle Swarm Optimization (WAIW-PSO) algorithm was proposed to solve the optimal offloading scheme quickly. Firstly, the task execution process of mobile terminal-cloudlet-remote cloud was modeled. Secondly, considering the competition of computing resources by multiple users, the task offloading model based on multi-cloudlet collaboration was constructed. Finally, since the complexity of solving the optimal offloading scheme was excessively high, the WAIW-PSO was proposed to solve the offloading problem. Simulation results show that compared with the standard Particle Swarm Optimization (PSO) algorithm and the PSO algorithm with Decreasing Inertia Weight based on Gaussian function (GDIWPSO), WAIW-PSO algorithm can adjust the inertia weight according to evolutionary generation and individual fitness, and it has the better optimization ability and the shortest time for finding the optimal offloading scheme. Experimental results on different task unloading schemes with different numbers of equipments and tasks show that the WAIW-PSO algorithm based offloading schemes can significantly shorten the total task completion time.

Key words: mobile cloud computing, cloudlet, task offloading, multi-cloudlet collaboration, Particle Swarm Optimization (PSO)

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