云计算环境下低消耗的任务调度方法仿真

发布时间：2019-05-27 06:13

【摘要】：在云计算环境下低消耗的任务调度时,由于云计算环境下低消耗的任务调度具有较高的复杂性和通用性,任务之间存在的多种特征,造成任务调度时受到多种制约条件。传统任务调度方法受到这种特征条件约束,导致任务调度效率降低,无法有效实现任务调度,提出基于改进免疫进化算法的任务调度方法,分析了一般免疫进化方法,将PSO算法看作是一个算子引入免疫进化算法中,对免疫算法进行改进,给出任务调度优化模型。在解空间中随机形成初始抗体,对种群规模、变异概率及最大迭代代数进行初始化处理。求出抗体群中各抗体的亲和度并进行排列,将某抗体进行变异,形成新抗体群,重新对亲和度进行计算和排列,从新抗体群中选择最佳抗体。通过PSO算子对新抗体进行处理,获取一组改进后的抗体,对亲和度较大的抗体进行统计。仿真结果表明,所提方法使云计算调度具有很高的效率。
[Abstract]:When the task scheduling with low consumption in cloud computing environment is low, because of the high complexity and universality of low consumption task scheduling in cloud computing environment, there are many characteristics between tasks, resulting in a variety of constraints in task scheduling. The traditional task scheduling method is constrained by this characteristic condition, which leads to the reduction of task scheduling efficiency and can not effectively realize task scheduling. A task scheduling method based on improved immune evolutionary algorithm is proposed, and the general immune evolution method is analyzed. The PSO algorithm is introduced into the immune evolutionary algorithm as an operator, and the immune algorithm is improved, and the optimization model of task scheduling is given. The initial antibody was randomly formed in the solution space, and the population size, variation probability and maximum iterative algebra were initialized. The affinity of each antibody in the antibody group was calculated and arranged, and a certain antibody was mutated to form a new antibody group. The affinity was calculated and arranged again, and the best antibody was selected from the new antibody group. A group of improved antibodies were obtained by PSO operator, and the antibodies with high affinity were counted. The simulation results show that the proposed method makes cloud computing scheduling highly efficient.
【作者单位】：华中科技大学计算机科学与技术学院;
【分类号】：TP18;TP393.07

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