Research on Cloud Computing Resource Scheduling Strategy Based on Firefly Optimized Genetic Algorithm

2019 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

无线电电子学;计算机科学;材料科学

Han, Yaning^1^2 ; Wang, Jinbo^2 ; Yao, Zhexi^1^2

University of Chinese Academy of Sciences, Beijing, China^1

Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China^2

关键词: Cloudsim;    Fitness functions;    Machine resources;    Optimization effects;    Resource-scheduling;    Scheduling models;    Task completion time;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/563/5/052104/pdf
DOI  :  10.1088/1757-899X/563/5/052104

来源: IOP

【 摘 要 】

In the cloud computing system environment, combined with the first-level scheduling model of task-virtual machine resource nodes, the individual coding, fitness function, selection replication and cross-variation process are redesigned, and the cloud computing resource scheduling model based on genetic algorithm is established. Corresponding to fireflies and virtual machine resource nodes, this paper redesigned the firefly decision domain update method, selected attraction probability formula and location movement strategy, and combined with genetic algorithm to establish cloud computing resource scheduling model based on firefly-genetic algorithm. Experiment with the CloudSim cloud computing simulation platform. The results show that the task completion time of the resource scheduling model is smaller than that of the single genetic algorithm. The virtual machine load is more balanced, the task completion time is short, and the overall optimization effect of the resource scheduling scheme is obvious.