Task Offloading Strategy in Mobile Edge Computing Based on Cloud-Edge-End Cooperation

Zhang Wenzhu; Yu Jinghua

doi:10.7544/issn1000-1239.202110803

Journal of Computer Research and Development > 2023 > 60(2): 371-385. > DOI: 10.7544/issn1000-1239.202110803

Zhang Wenzhu, Yu Jinghua. Task Offloading Strategy in Mobile Edge Computing Based on Cloud-Edge-End Cooperation[J]. Journal of Computer Research and Development, 2023, 60(2): 371-385. DOI: 10.7544/issn1000-1239.202110803

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Task Offloading Strategy in Mobile Edge Computing Based on Cloud-Edge-End Cooperation

Zhang Wenzhu,
Yu Jinghua^,

College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055

Funds: This work was supported by the National Natural Science Foundation of China (72071153), the Key Research and Development Program of Shanxi Province (2021GY-066), and the Natural Science Basic Research Plan of Shanxi Province (2020JM-489).

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Received Date: August 01, 2021
Revised Date: March 29, 2022
Available Online: February 10, 2023
Published Date: August 01, 2021

Graphical Abstract

Abstract

Abstract

In order to make use of limited computing resources to provide efficient computing services, a cloud-edge-end collaborative task offloading framework based on Docker is proposed In MEC (mobile edge computing) to solve the problems of multi-access MEC collaborative offloading and computing resource allocation. In order to improve the execution rate of tasks and the resource utilization of each node, preprocessing of tasks: Kahn algorithm added to the requirements of full rank matrix sets the execution sequence of output tasks in combination with the parallel calculation of tasks. Based on these, the task computing mathematical models of end, edge and cloud are established respectively, and the objective functions of the joint optimization system delay and energy consumption are designed by assigning weights. In order to solve the optimal offloading decision, the parameter of "full merit rate" and particle bee are introduced to propose APS (artificial particle swarm) algorithm as offloading decision algorithm. The experiments show that multi-task processing proves the effectiveness of APS algorithm. Compared with the five modes of local computing, edge computing, cloud computing, end-edge union computing and random processing, the low latency and low energy consumption of the proposed scheme proves its advantages in providing efficient services under multi-access conditions.

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References (41)

References

[1]	Casadei R, Fortino G, Pianini D, et al. Modelling and simulation of opportunistic IoT services with aggregate computing[J]. Future Generation Computer Systems, 2019, 91（2）: 252−262
[2]	Abolfazli S, Sanaei Z, Ahmed E, et al. Cloud-based augmentation for mobile devices: Motivation, taxonomies, and open challenges[J]. IEEE Communications Surveys Tutorials, 2014, 16(1): 337−368 doi: 10.1109/SURV.2013.070813.00285
[3]	崔勇,宋健,缪葱葱,等. 移动云计算研究进展与趋势[J]. 计算机学报,2017,40(2):273−295 Cui Yong, Song Jian, Liao Congcong, et al. Advances and trends in mobile cloud computing[J]. Chinese Journal of Computers, 2017, 40(2): 273−295 (in Chinese)
[4]	Haung Y R. A QoE-aware strategy for supporting service continuity in an MCC environment[J]. Wireless Personal Communications, 2021, 116(1): 629−654 doi: 10.1007/s11277-020-07731-2
[5]	周悦芝,张迪. 近端云计算: 后云计算时代的机遇与挑战[J]. 计算机学报,2019,42(4):677−700 doi: 10.11897/SP.J.1016.2019.00677 Zhou Yuezhi, Zhang Di. Near-end cloud computing: Opportunities and challenges in post-cloud computing era[J]. Chinese Journal of Computers, 2019, 42(4): 677−700 (in Chinese) doi: 10.11897/SP.J.1016.2019.00677
[6]	Mach P, Becvar Z. Mobile edge computing: A survey on architecture and computation offloading[J]. IEEE Communications Surveys & Tutorials, 2017, 19(3): 1628−1656
[7]	张开元,桂小林,任德旺,等. 移动边缘网络中计算迁移与内容缓存研究综述[J]. 软件学报,2019,30(8):2491−2516 doi: 10.13328/j.cnki.jos.005861 Zhang Kaiyuan, Gui Xiaolin, Ren Dewang, et al. A review of computing migration and content caching in mobile edge networks[J]. Journal of Software, 2019, 30(8): 2491−2516 (in Chinese) doi: 10.13328/j.cnki.jos.005861
[8]	Li Baogang, Si Fangqiang, Zhao Wei, et al. Wireless powered mobile edge computing with NOMA and user cooperation[J]. IEEE Transactions on Vehicular Technology, 2021, 70(2): 1957−1961 doi: 10.1109/TVT.2021.3051651
[9]	谢人超,廉晓飞,贾庆民,等. 移动边缘计算卸载技术综述[J]. 通信学报,2018,39(11):138−155 doi: 10.11959/j.issn.1000-436x.2018215 Xie Renchao, Lian Xiaofei, Jia Qingmin, et al. A review of moving edge computing offloading techniques[J]. Journal on Communications, 2018, 39(11): 138−155 (in Chinese) doi: 10.11959/j.issn.1000-436x.2018215
[10]	Zhang Yue, Fu Jingqi. Energy efficient computation offloading strategy with tasks scheduling in edge computing[J]. Wireless Networks, 2021, 27(1): 609−620 doi: 10.1007/s11276-020-02474-1
[11]	刘伟,黄宇成,杜薇,等. 移动边缘计算中资源受限的串行任务卸载策略[J]. 软件学报,2020,31(6):309−328 Liu Wei, Huang Yucheng, Du Wei, et al. A resource-constrained serial task offloading strategy in mobile edge computing[J]. Journal of Software, 2020, 31(6): 309−328 (in Chinese)
[12]	张礼庆,郭栋,吴绍岭,等. 一种最大化内存共享与最小化运行时环境的超轻量级容器[J]. 计算机研究与发展,2019,56(7):1545−1555 doi: 10.7544/issn1000-1239.2019.20180511 Zhang Liqing, Guo Dong, Wu Shaoling, et al. An ultra-lightweight container with maximum memory sharing and minimum runtime environment[J]. Journal of Computer Research and Development, 2019, 56(7): 1545−1555 (in Chinese) doi: 10.7544/issn1000-1239.2019.20180511
[13]	Tang Jie, Yu Rao, Liu Shaoshan, et al. A container based edge offloading framework for autonomous driving[J]. IEEE Access, 2020, 8(1): 33713−33726
[14]	Li Shuangyuan. A task offloading optimization strategy in MEC based smart cities[J]. Internet Technology Letters, 2021, 4(1): e158
[15]	黄倩怡,李志洋,谢文涛,等. 智能家居中的边缘计算[J]. 计算机研究与发展,2020,57(9):1800−1809 Huang Qianyi, Li Zhiyang, Xie Wentao, et al. Edge computing in the smart home[J]. Journal of Computer Research and Development, 2020, 57(9): 1800−1809 (in Chinese)
[16]	Xu Yu, Zhang Tiankui, Yang Dingcheng, et al. Joint resource and trajectory optimization for security in UAV assisted MEC systems[J]. IEEE Transactions on Communications, 2021, 69(1): 573−588 doi: 10.1109/TCOMM.2020.3025910
[17]	薛宁,霍如,曾诗钦,等. 基于DRL的MEC任务卸载与资源调度算法[J]. 北京邮电大学学报,2019,42(6):64−69 Xue Ning, Huo Ru, Zeng Shiqin, et al. MEC task unloading and resource scheduling algorithm based on DRL[J]. Journal of Beijing University of Posts and Telecommunications, 2019, 42(6): 64−69 (in Chinese)
[18]	Bolettieri S, Bruno R, Mingozzi E. Application-aware resource allocation and data management for MEC assisted IoT service providers[J]. Journal of Network and Computer Applications, 2021, 181(2): 103020
[19]	薛建彬,丁雪乾,刘星星. 缓存辅助边缘计算的卸载决策与资源优化[J]. 北京邮电大学学报,2020,43(3):32−37 Xue Jianbin, Ding Xuegan, Liu Xingxing. Offload decision and resource optimization for caches aided edge computing[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(3): 32−37 (in Chinese)
[20]	Liu Chubo, Tang Fang, Li Kenli, et al. Distributed task migration optimization in MEC by extending multi-agent deep reinforcement learning approach[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 32(7): 1603−1614
[21]	Meng Yao, Dai Janxin. Energy efficient joint computation offloading and resource allocation in multi-user MEC systems[J]. Journal of Physics: Conference Series, 2020, 1693: 012042
[22]	Miao Yiming, Wu Gaoxiang, Li Miao, et al. Intelligent task prediction and computation offloading based on mobile-edge cloud computing[J]. Future Generation Computer Systems, 2020, 102(9): 925−931
[23]	Li Yang, Xu Gaochao, Ge Jiaqi, et al. Energy efficient resource allocation for application including dependent tasks in mobile edge computing[J]. KSII Transactions on Internet and Information Systems, 2020, 14(6): 2422−2443
[24]	Chai Ming, Li Mingzhu, Yang Tiantian, et al. Dynamic priority based computation scheduling and offloading for interdependent tasks: Leveraging parallel transmission and execution[J]. IEEE Transactions on Vehicular Technology, 2020, 70(10): 10970−10985
[25]	Liu Bowen, Xu Xiaolong, Qi Lianyong, et al. Task scheduling with precedence and placement constraints for resource utilization improvement in multi-user MEC environment[J]. Journal of Systems Architecture, 2021, 114(6): 101970
[26]	Yan Jia, Bi Suzhi, Zhang Yingjun, et al. Optimal task offloading and resource allocation in mobile edge computing with inter-user task dependency[J]. IEEE Transactions on Wireless Communications, 2020, 19(1): 235−250 doi: 10.1109/TWC.2019.2943563
[27]	Chen Long, Wu Jigang, Zhang Jun, et al. Dependency-aware computation offloading for mobile edge computing with edge-cloud cooperation[J]. IEEE Transactions on Cloud Computing, 2020, 11(10): 973−991
[28]	Abbasi M, Mohammadi E, Khosravi M. Intelligent workload allocation in IoT-Fog-Cloud architecture towards mobile edge computing[J]. Computer Communications, 2021, 169(3): 71−80
[29]	Li Wenzao, Wang Fangxin, Pan Yuwen, et al. Computing cost optimization for multi-BS in MEC by offloading[J]. Mobile Networks and Applications, 2020, 25(4): 1628−1641
[30]	Zhu Zhengying, Qian Liping, Shen Jiafang, et al. Joint optimisation of UAV grouping and energy consumption in MEC enabled UAV communication networks[J]. IET Communications, 2020, 14(16): 2723−2730 doi: 10.1049/iet-com.2019.1179
[31]	Xie Renchao, Li Zishu, Wu Jun, et al. Energy-efficient joint caching and transcoding for HTTP adaptive streaming in 5G networks with mobile edge computing[J]. China Communications, 2019, 16(7): 229−244 doi: 10.23919/JCC.2019.07.017
[32]	Feng Siling, Chen Yinjie, Zhai Qianhao, et al. Optimizing computation offloading strategy in mobile edge computing based on swarm intelligence algorithms[J]. EURASIP Journal on Advances in Signal Processing, 2021, 2021(1): 1−15 doi: 10.1186/s13634-020-00710-6
[33]	Liu Zhizhong, Sheng Quan, Xu Xufei, et al. Context-aware and adaptive QoS prediction for mobile edge computing services[J]. IEEE Transactions on Services Computing, 2019, 30(9): 125−139
[34]	Liang Liang, Xiao Jintao, Ren Zhi, et al. Particle swarm based service migration scheme in the edge computing environment[J]. IEEE Access, 2020, 8: 45596−45606
[35]	Ma Shuyue, Song Shudian, Zhao Jingmei, et al. Joint network selection and service placement based on particle swarm optimization for multi-access edge computing[J]. IEEE Access, 2020, 8: 160871−160881
[36]	You Qian, Tang Bing. Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things[J]. Journal of Cloud Computing, 2021, 10(1): 1−11 doi: 10.1186/s13677-020-00210-w
[37]	Tamilselvan V. A hybrid PSO-ABC algorithm for optimal load shedding and improving voltage stability[J]. International Journal of Manufacturing Technology and Management, 2020, 34(6): 577−597 doi: 10.1504/IJMTM.2020.109999
[38]	Gerardo S, Andrade M, Lara-Velázquez P, et al. ABC-PSO: An efficient bioinspired metaheuristic for parameter estimation in nonlinear regression[C] //Proc of the 16th Mexican Int Conf on Artificial Intelligence. Cham: Springer, 2016: 388−400
[39]	Singh S, Chauhan P, Singh N. Capacity optimization of grid connected solarfuel cell energy system using hybrid ABC-PSO algorithm[J]. International Journal of Hydrogen Energy, 2020, 45(16): 10070−10088 doi: 10.1016/j.ijhydene.2020.02.018
[40]	Han Zidong, Li Yufeng, Liang Junyu. Numerical improvement for the mechanical performance of bikes based on an intelligent PSO-ABC algorithm and WSN technology[J]. IEEE Access, 2018, 6: 32890−32898
[41]	Zhou Ping, Shen Ke, Kumar N, et al. Communication efficient offloading for mobile edge computing in 5G heterogeneous networks[J]. IEEE Internet of Things Journal, 2020, 8(13): 10237−10247

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