基于改进蚁群算法的电力云数据中心任务调度策略研究

A task scheduling strategy for a power cloud data center based on an improved ant colony algorithm

针对现有云环境下电力数据中心任务调度的高能耗、低效率等问题,在电力云体系结构的基础上,提出了一种基于随机Petri网的云数据中心任务调度模型。通过综合考虑时间约束、负载、能耗约束对蚁群算法进行改进,并通过改进算法对模型进行求解。通过实验对运行时间、能耗、平均等待时间、系统负载等几个方面进行了比较分析,验证了该方法的优越性。结果表明,改进蚁群算法在保证性能的前提下,可以有效降低数据中心能耗,为电力数据中心任务调度策略的发展提供参考和借鉴。

There are problems of high energy consumption and low efficiency of power data center task scheduling in the existing cloud environment.Thus,based on power cloud architecture,this paper proposes a cloud data center task scheduling based on the stochastic Petri net model,and considers the time,load,and energy consumption constraints to improve the ant colony algorithm to solve the problem presented by the model.The advantages of this method are verified by comparing and analyzing the running time,energy consumption,average waiting time and system load.The results show that the improved ant colony algorithm can effectively reduce the energy consumption of the data center and guarantee performance.It provides a reference for the development of a task scheduling strategy in a power data center.