摘要
针对在大量数据背景下云计算资源调度模型存在调度效率低、分配不合理等问题,提出一种基于改进鲸鱼优化算法(m-WOA)的云计算资源调度方法。提出了云计算资源调度模型,针对基本鲸鱼优化算法存在迭代后期种群多样性减弱、易陷入局部最优等不足,提出使用Tent混沌反向学习策略来增强种群多样性;并使用精英随机组合策略平衡算法开发和探索能力。将改进后的m-WOA算法用于数值仿真实验和云计算资源调度模型求解。实验结果表明,m-WOA具有更好的收敛精度和更强的稳定性;m-WOA能有效减少云计算完成时间和能源消耗,并提供更合理的资源调度分配方案,从而提升云计算资源利用率。
A Cloud Computing Resource Scheduling method based on the improved Whale Optimization Algorithm(m-WOA)is proposed for the problems of low scheduling efficiency and unreasonable allocation in the cloud computing resource scheduling model in the context of big data.The Cloud Computing Resource Scheduling model is proposed to address the shortcomings of the basic whale optimization algorithm,such as weakened population diversity in the late iteration and easy to fall into local optimum,a Tent chaotic reverse learning strategy is proposed to enhance the population diversity.And the elite random combination strategy is used to balance the exploitation and exploration capability of the algorithm.Finally,the improved m-WOA is used for numerical simulation experiments and solving the cloud computing resource scheduling model.The experimental results show that m-WOA has better convergence accuracy and stronger stability.m-WOA can effectively reduce cloud computing completion time and energy consumption,and provide a more reasonable resource scheduling allocation schemes,thus improving cloud computing resource utilization.
作者
吴荣生
WU Rongsheng(Zhangzhou Institute of Technology,Zhangzhou,Fujian 363000,China)
出处
《龙岩学院学报》
2022年第2期20-26,共7页
Journal of Longyan University
基金
福建省中青年教师教育科研项目(JAT191418)
漳州职业技术学院校级课题(ZZY2021B029)。
关键词
云计算
资源调度
鲸鱼优化算法
函数测试
cloud computing
resource scheduling
whale optimization algorithm
function test
