An Algorithm for Cloud-based Web Service Combination Optimization Through Plant Growth Simulation  

一种基于模拟植物生长的Web云服务组合优化算法

作　　者：Li Qiang Qin Huawei Qiao Bingqin Wu Ruifang 李强;秦华伟;乔冰琴;吴瑞芳(山西省财政税务专科学校大数据学院,山西太原030027;太原理工大学财经学院,山西太原030027)

机构地区：[1]School of Big Data,Shanxi Finance&Taxation College,Taiyuan 030027,China [2]School of Finance and Economics,Taiyuan University of Technology,Taiyuan 030027,China

出　　处：《系统仿真学报》2025年第2期462-473,共12页Journal of System Simulation

基　　金：Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676);Shanxi Soft Science Program Research Fund Project(2016041008-6)。

摘　　要：In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.为提高web云服务的效率,提出一个改进的模拟植物生长算法调度模型。使用数学方法描述web云服务和系统资源之间的约束关系。建立了一个光诱导模型模拟植物生长。通过几种植物类型比较算法的性能,并选择最佳植物模型作为系统的设置。实验结果表明:当测试web云服务的数量达到2048时,该模型比PSO算法快2.14倍,比蚁群算法快2.8倍,比蜂群算法快2.9倍,比遗传算法快8.38倍。

关 键 词：cloud-based service scheduling algorithm resource constraint load optimization cloud computing plant growth simulation algorithm 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]