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.

References:: [1] CHEN Z Y, HE L G. Modelling task offloading mobileedge computing[C]∥Proceedingsof the 2022 8th International Conference on Computing and Data Engineering.New York:ACM, 2022: 15-21.
[2] BASLAIM O, AWANG A. Intelligent offloading decisionand resource allocation for mobile edge computing [ C]∥2022 International Conference on Future Trends in SmartCommunities ( ICFTSC) . Piscataway:IEEE, 2022: 204-209.
[3] 刘昊, 张景超, 毛万登, 等. 智慧换流站云边协同数据交互方法[ J] . 郑州大学学报( 工学版) , 2022, 43(5) : 104-110.
LIU H, ZHANG J C, MAO W D,et al. Cloud edge collaboration data interaction method of intelligent converterstation[ J] . Journal of Zhengzhou University ( Engineering Science) , 2022, 43(5) : 104-110.
[4] 刘振鹏, 王鑫鹏, 李明, 等. 基于时延和负载均衡的多控制器部署策略 [ J] . 郑州大学学报 ( 工学版) ,2021, 42(3) : 19-25, 32.
LIU Z P, WANG X P, LI M, et al. Multi-controller deployment strategy based on delay and load balancing[ J] .Journal of Zhengzhou University ( Engineering Science) ,2021, 42(3) : 19-25, 32.
[5] 欧阳聪, 关静, 杨鸣. 基于资源分配和动态分组的合作协同演化算法[ J] . 郑州大学学报(工学版) , 2023,44(5) : 10-16.
OUYANG C, GUAN J, YANG M. Cooperativeco-evolution algorithm based on resource allocation and dynamicgrouping[ J] . Journal of Zhengzhou University (Engineering Science) , 2023, 44(5) : 10-16.
[6] DRBHALAJI N. Efficient and secure data utilization inmobile edge computing by data replication[ J] . Journal ofISMAC, 2020, 2(1) : 1-12.
[7] YU Y, LI X, QIAN C. SDLB: a scalable and dynamicsoftware load balancer for fog and mobile edge computing[C]∥Proceedings of the Workshop on Mobile Edge Communications. New York: ACM, 2017: 55-60.
[8] GAO Y Q, LI Z M. Load balancing aware task offloadingin mobile edge computing[ C]∥2022 IEEE 25th International Conference on Computer Supported CooperativeWork in Design ( CSCWD) . Piscataway: IEEE, 2022:1209-1214.
[9] 张展, 张宪琦, 左德承, 等. 面向边缘计算的目标追踪应用部署策略研究[ J] . 软件学报, 2020, 31( 9) :2691-2708.
ZHANG Z, ZHANG X Q, ZUO D C, et al. Research ontarget tracking application deployment strategy for edgecomputing[ J] . Journal of Software, 2020, 31(9) : 2691-2708.
[10] 邝祝芳, 陈清林, 李林峰, 等. 基于深度强化学习的多用户边缘计算任务卸载调度与资源分配算法[ J] .计算机学报, 2022, 45(4) : 812-824.
KUANG Z F, CHEN Q L, LI L F, et al. Multi-user edgecomputing task offloading scheduling and resource allocation based on deep reinforcement learning [ J] . ChineseJournal of Computers, 2022, 45(4) : 812-824.
[11] FERNANDO N, LOKE S W, RAHAYU W. Computingwith nearby mobile devices: a work sharing algorithm formobile edge-clouds [ J ] . IEEE Transactions on CloudComputing, 2019, 7(2) : 329-343.
[12] LU H F, GU C H, LUO F, et al. Optimization of lightweight task offloading strategy for mobile edge computingbased on deep reinforcement learning[ J] . Future Generation Computer Systems, 2020, 102: 847-861.
[13] XU X L, FU S C, CAI Q, et al. Dynamic resource allocationfor load balancing in fog environment[J]. Wireless Communications and Mobile Computing, 2018, 2018: 6421607.
[14] ABBAS D B, LAVIN C V, FAHY E J, et al. Standardizing dimensionless cutometer parameters to determine invivo elasticity of human skin [ J ] . Advances in WoundCare, 2022, 11(6) : 297-310.
[15] 卢洪利. 基于博弈论模型的分布式系统的负载均衡与性能优化[D] . 天津: 天津理工大学, 2022.
LU H L. A game theoretical load balancing and optimization approach for distributed system[D] . Tianjin: TianjinUniversity of Technology, 2022.
[16] 曹洁, 曾国荪, 钮俊, 等. 云环境下可用性感知的并行任务调度方法[ J] . 计算机研究与发展, 2013, 50(7) : 1563-1572.
CAO J, ZENG G S, NIU J, et al. Availability-awarescheduling method for parallel task in cloud environment[ J] . Journal of Computer Research and Development,2013, 50(7) : 1563-1572.
[17] MOGI R, NAKAYAMA T, ASAKA T. Load balancingmethod for IoT sensor system using multi-access edgecomputing[ C] ∥2018 Sixth International Symposium onComputing and Networking Workshops ( CANDARW ) .Piscataway:IEEE, 2018: 75-78.
[18] ZHANG J, GUO H Z, LIU J J, et al. Task offloading invehicular edge computing networks: a load-balancing solution[ J] . IEEE Transactions on Vehicular Technology,2020, 69(2) : 2092-2104.
[19] SUN L T, LI Z G, LV J X, et al. Edge computing taskscheduling strategy based on load balancing[ J] . MATECWeb of Conferences, 2020, 309: 03025.
[20] SAMANTA A, LI Y. Latency-oblivious incentive serviceoffloading in mobile edge computing [ C] ∥2018 IEEE /ACM Symposium on Edge Computing ( SEC ) . Piscataway:IEEE, 2018: 351-353.

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Last Update: 2024-09-29