云环境下基于DO-GAPSO的任务调度算法

为了找到合理的云计算任务调度方案,仅从单一方面来优化调度策略已不能满足用户需求,但从多个方面优化调度策略又面临着权重分配问题。针对上述问题,从任务完成时间、任务完成成本、服务质量3个方面考虑,提出一种基于遗传与粒子群算法相融合的动态目标任务调度算法,在算法的适应度评价函数建模中引入线性权重动态分配策略。通过CloudSim平台进行云环境仿真实验,并将此算法与经典的双适应遗传算法(DFGA)、离散粒子群优化算法(DPSO)进行比较。实验结果表明,在相同的设置条件下,该算法在执行效率、寻优能力等方面优于其他两个算法,是一种云计算环境下有效的任务调度算法。

Task Scheduling Algorithm Based on DO-GAPSO under Cloud Environment

In order to find reasonable cloud computing task scheduling scheme, the demand of users can not be satisfied by optimizing scheduling strategy from a single aspect,and there are some weight assignment problems in several aspects to optimize scheduling policy.Focusing on the problems,considering the completion time,cost and service quality,an algorithm of a dynamic target based on particle swarm and genetic algorithm(DO-GAPSO) was proposed,a dynamic linear weighting allocation policy was introduced in the fitness of function modeling.Cloud environment simulation experiment was conducted in the CloudSim platform.Under the same condition,discrete particle swarm optimization(DPSO),double fitness genetic algorithm(DFGA) were compared with the proposed algorithm.The experimental results show that the proposed algorithm is better than the other two algorithms in execution efficiency and optimization ability.It is a kind of effective task scheduling algorithm in cloud computing environment.