云计算资源调度策略中最小资源矩阵应用的研究

摘要

传统的蚁群算法（ACO）在云计算资源调度的应用中，存在一些资源节点无法满足任务运行所需的硬件配置条件，从而在任务调度算法中造成了大量的浪费以及整体资源调度效率低下等问题。据此提出一种基于最小资源矩阵（ACO⁃MRM）的改进蚁群算法，抛弃大量不满足任务运行条件的资源节点，减少大量对无效资源节点的计算，加速算法收敛。仿真实验表明，改进的蚁群算法不仅能够提高云计算调度的有效性，而且能缩短任务执行时间和减少运行成本来获取全局最优调度方案。%The traditional ant colony optimization(ACO)algorithm in application of resource scheduling of the cloud com⁃puting has the shortage that some resource nodes can′t meet the hardware collocation condition needed by task⁃running,so huge amounts of waste and low integral resource scheduling efficiency are generated in task scheduling algorithm. To solve these prob⁃lems,an improved ACO algorithm based on the minimum resource matrix is proposed,by which the mass resource nodes which can′t satisfy the task running condition is abandoned to reduce the computation of mass invalid resource nodes and accelerate the algorithm convergence. The simulation experiment results show this improved ant colony optimization algorithm can improve the effectiveness of the cloud computing scheduling,shorten the task execution time and reduce running cost to obtain the global optimal scheduling scheme.