[1]曹 洁,贾连辉,许金超.云边环境下按需分配物理资源的任务卸载策略[J].郑州大学学报(工学版),2024,45(06):65-74.[doi:10.13705/j.issn.1671-6833.2024.03.015]
　CAO Jie,JIA Lianhui,XU Jinchao.The Mobile Task Offloading Strategy for Allocating Physical Resources onDemand in Dynamic Cloud-edge Environment[J].Journal of Zhengzhou University (Engineering Science),2024,45(06):65-74.[doi:10.13705/j.issn.1671-6833.2024.03.015]
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云边环境下按需分配物理资源的任务卸载策略()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年06期
页码:
65-74
栏目:
出版日期:
2024-09-25

文章信息/Info

Title:
The Mobile Task Offloading Strategy for Allocating Physical Resources onDemand in Dynamic Cloud-edge Environment
文章编号:
1671-6833(2024)06-0065-10
作者:
曹 洁1;  贾连辉1;  许金超2
1. 郑州轻工业大学 软件学院,河南 郑州 450002;2. 上海交通大学 医学院,上海 200240
Author(s):
CAO Jie1;  JIA Lianhui1;  XU Jinchao2
1. Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450002, China; 2. School of Medicine, Shanghai Jiaotong University, Shanghai 200240, China
关键词:
边缘服务器;  移动任务;  服务匹配度;  按需分配;  最优卸载决策
Keywords:
edge server;  mobile tasks;  service matching degree;  on-demand allocation;  optimal unloading decision
分类号:
TN929. 5TP391. 9
DOI:
10.13705/j.issn.1671-6833.2024.03.015
文献标志码:
A
摘要:
针对有限边缘服务器资源最大限度满足众多移动任务有着截止时间要求的移动任务卸载问题,提出了一种云边协同卸载移动任务的模型。 该模型首先分析了影响移动任务服务需求和虚拟机服务保障的因素并给出度量方法,以及移动任务与虚拟机的服务匹配度的度量方法。 其次,设计了一种动态云边环境下按需分配物理资源的移动任务卸载策略,该策略基于改进匈牙利算法求一批任务的最大化服务匹配度的卸载方案,并通过有限次迭代消除资源竞争进一步优化卸载方案。 最后,将所提算法与 P2PITS、ALBOA 和 ESSDSA 算法进行对比,结果表明:相对于 P2PITS 算法,所提算法虚拟机负载率降低了 30. 1%,平均等待时间降低了 13%;相对于 ALBOA 算法,所提算法平均完成时间降低了 38. 6%;相对于 ESSDSA 算法,所提算法执行成功率提高了 3. 5%。 所提算法能够在满足用户截止时间要求下有效提高资源利用率,降低任务的平均完成时间。
Abstract:
Aiming at the unloading problem of mobile tasks for the limited edge server resources to maximize thesatisfaction of numerous mobile tasks with deadline requirements, a model for cloud-edge-device collaboration wasproposed to offload mobile tasks. Firstly, the model analyze the factors that affect the service demand of mobiletasks and the service guarantee of virtual machines, and give the measurement method, as well as the measurementmethod of the service matching degree between mobile tasks and virtual machines. Secondly, a mobile task offloading strategy was designed for on-demand allocation of physical resources in a dynamic cloud-edge environment.Based on the improved Hungarian algorithm, the purpose of this strategy was to find an offloading plan that couldmaximize service matching for a batch of tasks, and to further optimize the offloading plan by eliminating resourcecompetition through a limited number of iterations. Finally, the algorithm in this study was compared with theP2PITS algorithm, the ALBOA algorithm and the ESSDSA algorithm from many aspects. Experimental resultsshowed that compared with the P2PITS algorithm, the algorithm in this study reduced the virtual machine load rateby 30. 1%, the average waiting time by 13%, compared with the ALBOA algorithm, the algorithm in this study reduce the average completion time by 38. 6% on average, compared with the ESSDSA algorithm, the algorithm inthis study increased the execution success rate by 3. 5% on average. The proposed algorithm could effectively improve resource utilization and reduce the average completion time of tasks while meeting user deadline requirements.

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更新日期/Last Update: 2024-09-29