Service placement strategies in mobile edge computing based on an improved genetic algorithm

被引：0
|
作者：
Zheng, Ruijuan [1 ]
Xu, Junwei [1 ]
Wang, Xueqi [1 ]
Liu, Muhua [1 ]
Zhu, Junlong [1 ]
机构：
[1] Henan Univ Sci & Technol, Sch Informat Engn, Luoyang 471023, Henan, Peoples R China
来源：
PERVASIVE AND MOBILE COMPUTING | 2024年 / 105卷
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Energy consumption; Genetic algorithm; Nonlinear function approximation; Mobile edge computing; Service placement;
D O I：
10.1016/j.pmcj.2024.101986
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In mobile edge computing (MEC), quality of service (QoS) is closely related to optimizing service placement strategies, which is crucial to providing efficient services that meet user needs. However, due to the mobility of users and the energy consumption limit of edge servers, the existing policies make it difficult to ensure the QoS level of users. In this paper, a novel genetic algorithm based on a simulated annealing algorithm is proposed to balance the QoS of users and the energy consumption of edge servers. Finally, the effectiveness of the algorithm is verified by experiments. The results show that the QoS value obtained by the proposed algorithm is closer to the maximum value, which has significant advantages in improving QoS value and resource utilization. In addition, in software development related to mobile edge computing, our algorithm helps improve the program's running speed.
引用
收藏
页数：16
相关论文
共 50 条
  • [1] AN EFFICIENT SERVICE MIGRATION MODEL BASED ON IMPROVED GENETIC ALGORITHM IN MOBILE EDGE COMPUTING ENVIRONMENT
    Zhang, Xiuguo
    Liu, Yufei
    Cao, Zhiying
    Zhou, Huijie
    Zhang, Fengge
    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2021, 17 (04): : 1401 - 1419
  • [2] An improved multi-objective genetic algorithm with heuristic initialization for service placement and load distribution in edge computing
    Maia, Adyson M.
    Ghamri-Doudane, Yacine
    Vieira, Dario
    de Castro, Miguel Franklin
    COMPUTER NETWORKS, 2021, 194
  • [3] Collaborative Service Placement for Mobile Edge Computing Applications
    Yu, Nuo
    Xie, Qingyuan
    Wang, Qiuyun
    Du, Hongwei
    Huang, Hejiao
    Jia, Xiaohua
    2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2018,
  • [4] An Efficient Service Function Chaining Placement Algorithm in Mobile Edge Computing
    Wang, Meng
    Cheng, Bo
    Chen, Junliang
    ACM TRANSACTIONS ON INTERNET TECHNOLOGY, 2020, 20 (04)
  • [5] Dynamic Service Placement Algorithm for Partitionable Applications in Mobile Edge Computing
    Lu, Kun
    Song, Jianyu
    Yang, Linlin
    Xu, Guorui
    Li, Mingchu
    2022 22ND IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING (CCGRID 2022), 2022, : 1036 - 1041
  • [6] Joint service placement and request routing in mobile edge computing
    Yuan, Binbin
    Guo, Songtao
    Wang, Quyuan
    AD HOC NETWORKS, 2021, 120
  • [7] Collaborative Service Placement for Maximizing the Profit in Mobile Edge Computing
    Zeng, Guotai
    Du, Hongwei
    Ye, Qiang
    Zhang, Chen
    2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021,
  • [8] Joint Edge Server Placement and Service Placement in Mobile-Edge Computing
    Zhang, Xinglin
    Li, Zhenjiang
    Lai, Chang
    Zhang, Junna
    IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (13) : 11261 - 11274
  • [9] Priority Based Service Placement Strategy in Heterogeneous Mobile Edge Computing
    Teng, Meiyan
    Li, Xin
    Qin, Xiaolin
    Wu, Jie
    ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2020, PT I, 2020, 12452 : 314 - 329
  • [10] Bandit Learning-based Service Placement and Resource Allocation for Mobile Edge Computing
    Lie, Wen
    He, Dazhi
    Huang, Yihang
    Zhang, Yizhe
    Xu, Yin
    Guan Yun-feng
    Zhang, Wenjun
    2020 IEEE 31ST ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (IEEE PIMRC), 2020,
12345