计算机与现代化 ›› 2022, Vol. 0 ›› Issue (05): 54-60.

• 网络与通信 • 上一篇    下一篇

一种适用于多任务多资源移动边缘计算环境下的改进粒子群算力卸载算法

  

  1. (1.广东松山职业技术学院计算机与信息工程学院,广东韶关512126;2.国父大学,菲律宾曼达路尤1550;
    3.一牧科技(北京)有限公司,北京100000;4.甘肃五环公路工程有限公司,甘肃兰州730000)
  • 出版日期:2022-06-08 发布日期:2022-06-08
  • 作者简介:张彦虎(1981—),男,甘肃白银人,广东松山职业技术学院高级工程师,菲律宾国父大学博士研究生,研究方向:算法研究,边缘计算,图像识别,E-mail: forzyh@163.com; 通信作者:鄢丽娟(1983—),女,江西丰城人,讲师,硕士,研究方向:算法研究,图像识别,无线网络,E-mail: juanjanny@qq.com; 马志愤(1982—),男,甘肃白银人,博士,研究方向:云计算,人工智能; 张彦军(1984—),男,甘肃白银人,工程师,学士,研究方向:算法研究,人工智能。
  • 基金资助:
    广东省普通高校特色创新项目资助(2019GKTSCX041); 广东省高职教育精品课程建设项目资助(粤教职函[2018]194.50); 广东省韶关市科技计划(社会发展与农村科技专项)资金项目资助(韶科〔2018〕133号-2018SN041)

An improved Particle Swarm Optimization (PSO) Force Unloading Algorithm for Multi-task and Multi-resource Moving Edge Computing Environment

  1. (1. School of Computer and Information Engineering, Guangdong Songshan Politechnic, Shaoguan 512126, China;
    2. Jose Riazal University, Mandaluyong 1550, Philippines; 3. Yimu Technology (Beijing) Co., Ltd., Beijing 100000, China;
    4. Gansu Wuhuan Highway Engineering., Ltd., Lanzhou 730000, China)
  • Online:2022-06-08 Published:2022-06-08

摘要: 为了研究移动设备在多资源复杂环境下的能量消耗问题,提出一种针对移动边缘设备计算卸载的改进粒子群算法。首先基于多环境的移动设备能耗提出一种移动设备能量消耗的计算模型;其次针对计算资源分配问题设计一种可以用于衡量分配方案优劣的适应度算法;最后提出一种改进的粒子群算法,用于求解进一步降低移动边缘设备能耗分配方案的最优解。通过使用模拟仿真软件对多种卸载策略下移动设备能耗、系统响应时间等关键指标对比表明,本文算法在满足用户响应时间的前提下,在求解降低移动设备能耗调度分配方案最优解的过程中具有更优的表现。

关键词: 移动边缘计算, 移动边缘设备, 计算卸载, 粒子群算法, 适应度, 能耗模型, 能源效率

Abstract: In order to study the energy consumption of mobile devices in multi-resource complex environment, an improved particle swarm optimization (PSO) algorithm for unloading calculation of mobile edge devices is proposed. Firstly, a computing model is proposed based on the multi-environment energy consumption of mobile devices.Secondly, a fitness algorithm is designed to measure the advantages and disadvantages of resource allocation schemes for computing resource problems.Finally, an improved particle swarm optimization (PSO) algorithm for energy allocation is presented for solving the optimal solution to further reduce the energy consumption scheduling and allocation scheme of mobile devices.The comparison of energy consumption system response time and other indicators of mobile devices under various unloading strategies by simulation software shows that the proposed algorithm has a better performance in solving the optimal solution to reduce the energy consumption scheduling and allocation scheme of mobile devices on the premise of satisfying the user response time.

Key words: mobile edge computing, mobile edge devices, computation offloading, particle swarm optimization, fitness, energy consumption model, energy efficient