摘要
云工作流系统研究集中在工作流任务执行的时间效率优化,然而时间最优的任务调度方案可能存在不同能耗,因此,文中求解满足时间约束时能耗最优的调度方案.首先改进任务执行能耗模型,设计适用于评价任务调度方案执行能耗的适应度计算方法.然后基于精准调整粒子速度的自适应权重,提出解决任务调度能耗优化问题的自适应粒子群算法.实验表明,文中算法收敛稳定,调度方案执行能耗较低.
In the research on cloud workflow systems, the time efficiency optimization of the task execution is the emphasis. The energy consumption optimization of the task execution is often ignored. However, time-optimal task scheduling plans have different energy consumption. Therefore, how to solve energy-optimal task scheduling plans with time constraint are discussed in this paper. Firstly, the energy model of task execution is improved. Then, the fitness computation method of the task plan is designed to evaluate energy consumption. Finally, an adaptive inertia weight computation method is applied to adjust particle velocity accurately and a particle swarm optimization （ PSO） algorithm is presented to solve the energy consumption optimization problem of task scheduling in cloud workflow systems. Experimental results show that the proposed algorithm has a stable convergence speed with low energy consumption.
作者
李学俊
徐佳
王福田
朱二周
吴蕾
LI Xuejun XU Jia WANG Futian ZHU Erzhou WU Lei(School of Computer Science and Technology, Anhui University, Hefei 230601)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2016年第9期790-796,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61672034)
教育部社科研究青年基金项目(No.16YJCZH048)
安徽省教育厅自然科学研究重点项目(No.KJ2016A024)资助~~
关键词
云计算
工作流调度
绿色计算
粒子群优化
惯性权重
Cloud Computing
Workflow Scheduling
Green Computing
Particle Swarm Optimization
Inertia Weight
