基于动态遗传算法的云计算任务节能调度策略研究

Energy-efficient Cloud Task Scheduling Research based on Dynamic Genetic Algorithm

云计算的飞速发展造成了许多大型数据中心的建立,海量的数据中心会消耗巨大的电力能源,导致操作成本以及二氧化碳排放量的升高。为了解决这一问题,本文提出了一种基于遗传算法的新型多目标动态调度算法,将任务的执行时间及数据中心的能耗作为优化目标,充分考虑云环境的动态性,根据任务长度以及资源计算能力将任务分配给资源。本文将该算法与一些著名的云调度模型进行对比,实验结果证明,本文提出的多目标动态遗传算法可以有效利用于云环境,并在减少任务执行时间和能耗方面具有一定优势。

The rapid development of cloud computing has led to the establishment of large- scale data centers. Such data centers consume enormous amounts of electric energy,resulting in high operating cost and carbon dioxide emissions. With the aid of traditional genetic algorithm,the paper presents a new multi- objective dynamic scheduling algorithm,which considers cloud environment dynamics and reduces total execution time and power consumption. The new algorithm assigns the jobs to the resources according to the job length and resources capacities. After that,the paper evaluates this algorithm with some famous cloud scheduling algorithm,and the experiments show the efficiency of the proposed approach in terms of execution time,power consumption in cloud environment.