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

被引：2

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

页数：16

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