通信学报 ›› 2015, Vol. 36 ›› Issue (1): 149-158.doi: 10.11959/j.issn.1000-436x.2015017

• 学术论文 • 上一篇    下一篇

面向数据密集型工作流的能耗感知调度策略

肖鹏1,胡志刚2,屈喜龙1   

  1. 1 湖南工程学院 计算机与通信系,湖南 湘潭 411104
    2 中南大学 软件学院,湖南 长沙 410083
  • 出版日期:2015-01-25 发布日期:2017-06-21
  • 基金资助:
    国家自然科学基金资助项目;国家自然科学基金资助项目;湖南省教育厅科学研究基金资助项目;湖南省科技计划项目基金资助项目;湖南省自然科学基金资助项目

Energy-aware scheduling policy for data-intensive workflow

Peng XIAO1,Zhi-gang HU2,Xi-long QU1   

  1. 1 Department of Computer and Communication,Hunan Institute of Engineering,Xiangtan 411104,China
    2 School of Software,Central South University,Changsha 410083,China
  • Online:2015-01-25 Published:2017-06-21
  • Supported by:
    The National Natural Science Foundation of China;The National Natural Science Foundation of China;The Scientific Research Fund of Hunan Provincial Education Department;Provincial Science & Technology Plan Project of Hunan;Hunan Provincial Natural Science Foundation of China

摘要:

随着数据中心规模的扩大,高能耗问题已经成为高性能计算领域的一个重要问题。针对数据密集型工作流的高能耗问题,提出通过引入“虚拟数据访问节点”的方法来量化评估工作流任务的数据访问能耗开销,并在此基础上设计了一种“最小能耗路径”的启发式策略。在经典的HEFT算法和CPOP算法基础上,通过引入该启发式策略设计并实现了2种具有能耗感知能力的调度算法(HEFT-MECP和CPOP-MECP)。实验结果显示,基于最小能耗路径的启发式调度算法能有效降低数据访问操作的能耗开销,在面对大型的数据密集工作流任务时,该启发式调度策略体现了较好的适应性。

关键词: 工作流, 能耗, 启发式策略, 云计算

Abstract:

With the increasing scale of data centers,high energy consumption has become a critical issue in high-performance computing area.To address the issue of energy consumption optimization for data-intensive workflow applications,a set of virtual data-accessing nodes are introduced into the original workflow for quantitatively evaluating the data-accessing energy consumption,by which a novel heuristic policy called minimal energy consumption path is designed.Based on the proposed heuristic policy,two energy-aware scheduling algorithms are implemented,which are deprived from the classical HEFT and CPOP scheduling algorithms.Extensive experiments are conducted to investigate the performance of the proposed algorithms,and the results show that they can significantly reduce the data-accessing energy consumption.Also,the proposed algorithms show better adaptive when the system is in presence of large-scale workflows.

Key words: workflow, energy consumption, heuristic policy, cloud computing