一种基于遗传算法的多边缘协同计算卸载方案

doi:10.11896/jsjkx.200800088

计算机科学 ›› 2021, Vol. 48 ›› Issue (1): 72-80.doi: 10.11896/jsjkx.200800088

所属专题：智能化边缘计算

一种基于遗传算法的多边缘协同计算卸载方案

高基旭, 王珺

南京邮电大学通信与信息工程学院南京 210000

收稿日期:2020-08-16 修回日期:2020-10-18 出版日期:2021-01-15 发布日期:2021-01-15
通讯作者: 王珺(wang_jun@njupt.edu.cn)
作者简介:gjxaiou@gmail.com

Multi-edge Collaborative Computing Unloading Scheme Based on Genetic Algorithm

GAO Ji-xu, WANG Jun

School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210000,China

Received:2020-08-16 Revised:2020-10-18 Online:2021-01-15 Published:2021-01-15
About author:GAO Ji-xu,born in 1996,postgraduate.His main research interests include mobile edge computing and IoT.
WANG Jun,born in 1975,Ph.D,asso-ciate professor.Her main research inte-rests include network architecture of IoT and wireless sensor networks.

摘要/Abstract

摘要： 边缘计算(Edge Computing,EC)作为云计算的补充,在处理 lOT 设备产生的计算任务时可以保证计算的延时符合系统的要求。针对在传统卸载场景中,由于计算任务到达存在空窗期导致异地边缘云存在空闲状态,造成异地边缘云利用不充分的问题,文中提出了一种基于遗传算法的多边缘与云端协同计算卸载模型(Genetic Algorithm-based Multi-edge Collaborative Computing Offloading Model,GAMCCOM)。该计算卸载方案联合本地边缘和异地边缘进行任务卸载,并采用遗传算法进行求解,从而得到同时考虑时延和能耗的最小的系统代价。通过仿真实验结果可知,在综合考虑卸载系统的时延消耗和能量消耗的情况下,该方案相比基本的三层卸载方案系统整体代价降低了23%,在只考虑时延消耗和只考虑能量消耗的情况下依然分别能够降低系统代价 17% 和 15%。因此针对边缘计算的不同卸载目标,GAMCCOM 卸载方案对系统代价均有比较优秀的降低效果。

关键词: 边缘计算, 计算卸载, 物联网, 协同卸载, 遗传算法

Abstract: As a supplement to cloud computing,edge computing can ensure that the calculation delay meets system requirements when processing computing tasks generated by lOT equipment.Aiming at the problem of insufficient utilization of the remote edge cloud due to the empty window period of the computing task in the traditional offloading scenario,a genetic algorithm-based multi-edge and cloud collaborative computing offloading model (Genetic Algorithm-based Multi-edge Collaborative Computing Offloading Model,GAMCCOM) is proposed.This computing offloading solution combines local edge and remote edge to perform task offloading and uses a genetic algorithm to get the minimum system cost under consideration of both delay and energy consumption at the same time.The results of simulation experiments show that when considering the time delay consumption and energy consumption of the unloading system,the overall cost of this scheme is reduced by 23% compared with the basic three-layer unloading scheme.In the case of considering time delay consumption and energy consumption respectively,the system cost can still be reduced by 17% and 15% respectively.Therefore,the GAMCCOM offloading method can effectively reduce the system cost for different offloading targets of edge computing.

Key words: Collaborative unloading, Computing unloading, Edge computing, Genetic algorithm, Internet of things

中图分类号:

TP393

高基旭, 王珺. 一种基于遗传算法的多边缘协同计算卸载方案[J]. 计算机科学, 2021, 48(1): 72-80. https://doi.org/10.11896/jsjkx.200800088

GAO Ji-xu, WANG Jun. Multi-edge Collaborative Computing Unloading Scheme Based on Genetic Algorithm[J]. Computer Science, 2021, 48(1): 72-80. https://doi.org/10.11896/jsjkx.200800088

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一种基于遗传算法的多边缘协同计算卸载方案

Multi-edge Collaborative Computing Unloading Scheme Based on Genetic Algorithm

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