胡志刚1,常健1, 周舟2.面向云环境中任务负载的粒子群优化调度策略[J].湖南大学学报:自然科学版,2019,(8):117~123
面向云环境中任务负载的粒子群优化调度策略
PSO Scheduling Strategy for Task Load in Cloud Computing
  
DOI:
中文关键词:  云计算  任务调度  惯性权重  粒子群优化
英文关键词:cloud computing  task scheduling  inertia weight  Particle Swarm Optimization(PSO)
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作者单位
胡志刚1,常健1, 周舟2 (1. 中南大学 计算机学院湖南 长沙 410075 2. 长沙学院 计算机工程与应用数学学院湖南 长沙 410022) 
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中文摘要:
      随着云环境中任务规模的不断扩大,云计算中心高能耗问题变得日益突出.如何解决云环境中任务分配问题从而有效降低能耗,本文提出了一种改进的粒子群优化算法(Modified Particle Swarm Optimization, M-PSO).首先构建出一个云计算能耗模型,同时考虑处理器的执行能耗和任务传输能耗.基于该模型,对任务分配问题进行定义描述,并采用粒子群优化算法对问题进行求解.此外,构建动态调整的惯性权重系数函数以克服标准PSO算法的局部最优和收敛速度慢的问题,有效提高系统性能.最后通过仿真实验对该算法模型的性能进行了评估,结果表明M-PSO算法与其他算法相比能有效地降低系统总能耗.
英文摘要:
      As the scale of tasks in the cloud environment continues to expand, the problem of high energy consumption in cloud computing centers has become increasingly prominent. In order to solve the problem of task assignment in a cloud environment and to effectively reduce energy consumption, a Modified Particle Swarm Optimization algorithm (M-PSO) was proposed. First, a cloud computing energy consumption model, which takes into account the processor's execution energy consumption and task transmission energy consumption, was introduced. Based on the model, the task assignment problem was defined and described, and the particle swarm optimization algorithm was used to solve this problem. In addition, a dynamically adjusted inertia weight coefficient function was constructed to overcome the local optimization and slow convergence problem of the standard PSO algorithm, and the strategy can effectively improve the system performance. Finally, the performance of the introduced algorithm model was evaluated by simulation experiments. The results show that the M-PSO algorithm can effectively reduce the total energy consumption of the system compared with other algorithms.
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