摘要
针对现有云计算任务调度算法收敛速度慢、求解效率低和用户服务质量要求日益多样化等挑战,结合遗传算法和蚁群算法的优点,提出一种考虑用户和服务商满意度的混合遗传蚁群算法处理云计算任务调度问题,包括构建云计算任务调度模型和遗传蚁群算法的融合机制。该算法首先利用改进遗传算法得到云计算任务调度问题的初解,将其作为蚁群算法的信息素分布依据,其次通过改进蚁群算法的寻径规则、信息素初始化方式、信息素更新规则及信息素约束规则,得到云计算任务调度问题的最终解,最后在CloudSim平台上,使用所提算法进行实验仿真,并将结果与遗传算法、蚁群算法进行比较。实验表明,该算法提高了云计算任务调度的效率和可靠性,同时在减少任务执行时间、节约成本和服务质量方面有明显的优势。
To address the challenges of slow convergence speed,low solution efficiency of existing cloud computing task scheduling algorithms,and the increasingly diversified quality of service(QoS)requirements from users,this paper proposes a hybrid genetic ant colony algorithm(HGACA)that considers both user and service provider satisfaction for cloud computing task scheduling by integrating the advantages of genetic algorithm(GA)and ant colony algorithm(ACA).The approach includes constructing a cloud computing task scheduling model and a fusion mechanism for the genetic-ant colony algorithm.The algorithm first uses an improved genetic algorithm to obtain an initial solution for the cloud computing task scheduling problem,which is then used as the basis for pheromone distribution in the ant colony algorithm.Secondly,by improving the path-finding rules,pheromone initialization,pheromone update rules,and pheromone constraint rules of the ant colony algorithm,the final solution to the cloud computing task scheduling problem is derived.Finally,the proposed algorithm is experimentally simulated on the CloudSim platform,and the results are compared with those of the genetic algorithm and ant colony algorithm.Experiments show that the algorithm improves the efficiency and reliability of cloud computing task scheduling,and has obvious advantages in reducing task execution time,saving costs,and enhancing quality of service.
作者
任小强
刘贺
蒋玉香
陈丹
REN Xiaoqiang;LIU He;JIANG Yuxiang;CHEN Dan(Southwest Jiaotong University Hope College,Chengdu,Sichuan,China 610400)
出处
《深圳信息职业技术学院学报》
2025年第3期79-90,共12页
Journal of Shenzhen Institute of Information Technology
基金
成都市哲学社会科学重点研究基地成都市交通+旅游大数据应用技术研究基地基金项目(项目编号:20231012)
四川省教育信息技术基金资助项目(项目编号:2024KTPSLX280)
教育部高等教育司供需对接就业育人基金资助项目(项目编号:2024082786929,2024082768545)
西南交通大学希望学院校级质量工程基金资助项目(项目编号:2023036)
中国电子劳动学会基金资助项目(项目编号:Ceal2024108)。
关键词
云计算任务调度
用户服务质量
蚁群优化算法
遗传算法
负载均衡
cloud computing task scheduling
quality of service
ant colony optimization algorithm
genetic algorithm
load balance
