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

机译:基于萤火虫优化遗传算法的云计算资源调度策略研究

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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.
机译:在云计算系统环境中,与任务虚拟机资源节点的第一级调度模型组合,单个编码,健身功能,选择复制和交叉变化过程是重新设计的,以及基于遗传算的云计算资源调度模型建立算法。对应于萤火虫和虚拟机资源节点,本文重新设计了萤火虫决定域更新的方法,选择的吸引力概率公式和位置运动策略,以及与遗传算法相结合以建立云计算基于资源调度模型萤火虫遗传算法。实验Cloudsim云计算仿真平台。结果表明,资源调度模型的任务完成时间小于单个遗传算法的任务完成时间。虚拟机负载更加平衡,任务完成时间短,资源调度方案的整体优化效果是显而易见的。

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