摘要
为了改善云资源调度的性能,解决水波优化算法因水波衰减系数和碎波系数设置不当而导致精度降低的问题,提出人工蜂群-水波优化算法,采用人工蜂群算法对水波衰减系数和碎波系数进行参数寻优求解;在初始化任务实例生成的样本集和水波个体后,采用3个优化指标的加权和作为适应度函数,构建基于人工蜂群-水波优化算法的云资源调度模型,将最优求解问题转变为最优水波个体问题;通过不断更新最优适应度个体,提高云资源调度适用性,以达到最大迭代次数时所获得的最优云资源调度参数组合作为最优个体。结果表明:与常规水波优化算法相比,当任务数为600时,所提出算法的承载任务量分布更均匀,且负载均衡指标更小,仅为1.71;与基于其他智能优化算法的云资源调度模型相比,所建立模型所需执行时间最短,且稳定性更好。
To improve the performance of cloud resource scheduling and solve the problem that the accuracy of water wave optimization algorithm was reduced due to the improper setting of water wave attenuation coefficients and fragmentation coefficients,an artificial bee colony-water wave optimization algorithm was proposed,and artificial bee colony algorithm was used to solve the water wave attenuation coefficients and fragmentation coefficients by seeking the optimal parameters.After initializing the sample set and water wave individuals generated by the task instance,the weighted sum of the three optimization indicators was used as the fitness,the cloud resource scheduling model based on artificial bee colony-water wave optimization algorithm was constructed to transform the optimal solution problem into the optimal water wave individual problem.By constantly updating the optimal fitness individual,the applicability of cloud resource scheduling was improved,and the optimal combination of cloud resource scheduling parameters obtained when the maximum number of iterations was reached as the optimal individual.The results show that when the number of tasks is 600,the proposed algorithm has a more uniform distribution of the number of tasks and a smaller load balancing index of 1.71 compared with conventional water wave optimization algorithms.Compared with the cloud resource scheduling models based on other intelligent optimization algorithms,the proposed model requires the shortest execution time and is more stable.
作者
宋颖
梁卫芳
赵珏
张福泉
侯小毛
SONG Ying;LIANG Weifang;ZHAO Jue;ZHANG Fuquan;HOU Xiaomao(School of Computer Science and Engineering,Hunan University of Information Technology,Changsha 410151,Hunan,China;School of Computer Science,Hunan University of Technology and Business,Changsha 410205,Hunan,China;College of Computing and Information Technologies,National University,Manila 0900,Philippines;College of Computer and Control Engineering,Minjiang University,Fuzhou 350108,Fujian,China;Automation and Information Engineering College,Hunan Chemical Vocational Technology College,Zhuzhou 412000,Hunan,China)
出处
《济南大学学报（自然科学版）》
CAS
北大核心
2023年第4期472-477,498,共7页
Journal of University of Jinan(Science and Technology)
基金
国家自然科学基金项目(61871204)
福建省科技厅引导性项目(2018H0028)
湖南省教育厅科学研究项目(18A0295,20C0108)
湖南省教育科学规划项目(ND210887)。
关键词
云计算
资源调度
水波优化算法
人工蜂群算法
负载均衡
cloud computing
resource scheduling
water wave optimization
artificial bee colony
load balancing
