The Mobile Task Offloading Strategy for Allocating Physical Resources onDemand in Dynamic Cloud-edge Environment

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The Mobile Task Offloading Strategy for Allocating Physical Resources onDemand in Dynamic Cloud-edge Environment

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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 06 Page number: 65-74 Column: Public date: 2024-09-25

Title:: The Mobile Task Offloading Strategy for Allocating Physical Resources onDemand in Dynamic Cloud-edge Environment

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

Keywords:: edge server; mobile tasks; service matching degree; on-demand allocation; optimal unloading decision

CLC:: TN929. 5TP391. 9

DOI:: 10.13705/j.issn.1671-6833.2024.03.015

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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Last Update: 2024-09-29