基于混合策略鲸鱼优化算法的云计算任务调度研究

Research on Cloud Computing Task Scheduling Based on Mixed Strategy Whale Optimization Algorithm

针对云计算任务调度过程中存在任务执行时间长、系统执行成本过高及系统负载不均衡等问题,提出一种基于混合策略鲸鱼优化算法(MSWOA)的云计算任务调度方法。首先,使用Tent混沌映射初始化鲸鱼种群以提升种群多样性,使鲸鱼个体分布更均匀;其次,提出一种自适应概率阈值以平衡算法的全局搜索能力与局部开发能力,并在算法随机搜索阶段引入莱维飞行策略,扩大算法搜索空间与搜索能力;最后,设计了任务调度过程中的多目标适应度函数,并利用算法求解云计算多目标任务调度问题。通过CloudSim云计算仿真软件对MSWOA进行仿真实验,并将MSWOA与NOA、ZOA、OAWOA、TSWOA算法进行比较,实验结果表明,MSWOA相较于其他算法,在不同任务规模上都取得了更好的效果,不仅降低了任务最大完工时间和系统执行成本,还提升了系统平均负载率,在云计算多目标任务调度中优势显著。

A cloud computing task scheduling method based on the Hybrid Strategy Whale Optimization Algorithm(MSWOA)is proposed to address issues such as long task execution time,high system execution costs,and imbalanced system loads in the process of cloud computing task scheduling.Firstly,use Tent chaotic mapping to initialize the whale population to enhance population diversity and make the distribution of whale individuals more uniform;Then,an adaptive probability threshold was proposed to balance the global search capability and local development capability of the algorithm,and the Levy flight strategy was introduced in the random search stage of the algorithm to expand the search space and search capability of the algorithm;Finally,a multi-objective fitness function was designed for the task scheduling process,and an algorithm was used to solve the multi-objective task scheduling problem in cloud computing.The simulation experiment of MSWOA was conducted using CloudSim cloud computing simulation software,and the results of comparing MSWOA with NOA,ZOA,OAWOA,and TSWOA algorithms showed that compared with other algorithms,MSWOA achieved better performance at different task scales.It not only reduced the maximum completion time and system execution cost of tasks,but also improved the average load rate of the system,which has significant advantages in multi-objective task scheduling in cloud computing.