基于改进蚁群算法的自适应云资源调度模型研究

对于传统蚁群算法用于云计算资源分配和调度问题过程中存在的不足,提出了一种可以提高负载均衡度、缩短任务执行时间、降低任务执行成本的改进自适应蚁群算法,改进算法以能够基于用户提交的任务求解出执行时间较短、费用较低,负载率均衡的分配方案为目标,通过CloudSim平台对传统蚁群算法、最新的AC-SFL算法、改进自适应蚁群算法进行仿真实验对比。实验数据表明,改进后的自适应蚁群算法能够快速找出最优的云计算资源调度问题的解决方案,缩短了任务完成时间,降低了执行费用,保持了整个云系统中心的负载均衡。

Adaptive Cloud Resource Scheduling Model Based on Improved Ant Colony Algorithm

To address the shortcomings of the standard ant colony algorithm in cloud-computing resource allocation and scheduling,this study proposes an adaptive ant colony algorithm to improve load balance and reduce task execution time and costs.The proposed algorithm can solve tasks submitted by users with a short execution time,low cost,and balanced load rate.The traditional ant colony algorithm,the latest AC-SFL algorithm,and the improved adaptive ant colony algorithm are simulated using the CloudSim platform.Experimental results show that,the improved adaptive ant colony algorithm is able to quickly find a solution for the optimal cloud computing resource scheduling,shorten task completion time,reduce execution cost,and maintain the load balance of the entire cloud system center.