基于改进GEP和资源改变量的局部云任务调度算法

针对云计算中一些现有的基于批量调度模式和进化算法的动态云任务调度算法计算量较大,计算时间成本较高的现象,提出了一种基于改进基因表达式编程(GEP)和资源改变量的局部云任务调度算法.首先结合云任务调度的特点对普通GEP算法做出了相应的改进,然后采用加权求和的方式构造了一个基于综合利用率和能耗的适应度函数,最后依据物理机综合利用率的差异给出了基于改进GEP和资源改变量的局部云任务调度算法.基于资源改变量的局部云任务调度算法,通过对任务运行情况和物理资源使用情况进行监控,合理设定阈值,以减少参与调度物理机的个数,从而降低任务调度算法的时间成本.基于RH(rolling horizon)模型,通过实验将所提出的算法与普通遗传算法、全局GEP算法进行了比较,可知该算法不仅可以降低寻优时间,不易陷入局部最优解,且具有较快的收敛速度.

Local Cloud Task Scheduling Algorithm Based on Improved GEP and Change of Resources

Cloud computing has become the focus information processing and many related fields with its powerful computing and storage capacity. For some of the existing phenomenon about the large calculation and high computing time cost in cloud task scheduling algorithms based on the batch model and evolution algorithm, this paper presents a local cloud task scheduling algorithm based on improved GEP and change of resources. In the process of designing the algorithm, it is first improved the GEP algorithm according to the characteristics of cloud task scheduling. And then, this paper constructs a fitness function considering both the comprehensive utilization and energy consumption. Finally, this paper constructs a local cloud task scheduling algorithm based on improved GEP and comprehensive utilization. The algorithm proposed in this paper reduces the computing time cost by monitoring the physical resource usage and reducing the number of physical machines involved in the task scheduling. The comparison experiments among GEP, genetic algorithm and the algorithm proposed in this paper based on RH (rolling horizon) model has been made. The results show that the proposed algorithm can not only reduce the optimization time, hard to fall into the local optimal solution, but also has the faster convergence speed.