面向数据密集型工作流的能耗感知调度策略

肖鹏; 胡志刚; 屈喜龙

doi:10.11959/j.issn.1000-436x.2015017

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面向数据密集型工作流的能耗感知调度策略

学术论文 | 更新时间：2024-06-05

- 面向数据密集型工作流的能耗感知调度策略
- Energy-aware scheduling policy for data-intensive workflow
- 通信学报 2015年36卷第1期页码：149-158
- 作者机构：
  
  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
- 作者简介：
  
  [ "肖鹏（1979-），男，湖南湘潭人，博士，湖南工程学院讲师，主要研究方向为高性能网格计算和可信计算。" ]
  [ "胡志刚（1963-），男，山西孝义人，中南大学教授、博士生导师，主要研究方向为分布式系统和嵌入式系统。" ]
  [ "屈喜龙（1978-），男，湖南新邵人，博士，湖南工程学院副教授，主要研究方向为服务计算和网络集成系统。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61402163);国家自然科学基金资助项目(61272148);湖南省教育厅科学研究基金资助项目(13B015);湖南省科技计划项目基金资助项目(2012GK3075);湖南省自然科学基金资助项目(13JJ9022)
- DOI：10.11959/j.issn.1000-436x.2015017
  中图分类号： TP393
- 网络出版日期：2015-01，
  
  纸质出版日期：2015-01-25
- 稿件说明：
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肖鹏, 胡志刚, 屈喜龙. 面向数据密集型工作流的能耗感知调度策略[J]. 通信学报, 2015,36(1):149-158.

Peng XIAO, Zhi-gang HU, Xi-long QU. Energy-aware scheduling policy for data-intensive workflow[J]. Journal on communications, 2015, 36(1): 149-158.
肖鹏, 胡志刚, 屈喜龙. 面向数据密集型工作流的能耗感知调度策略[J]. 通信学报, 2015,36(1):149-158. DOI： 10.11959/j.issn.1000-436x.2015017.

Peng XIAO, Zhi-gang HU, Xi-long QU. Energy-aware scheduling policy for data-intensive workflow[J]. Journal on communications, 2015, 36(1): 149-158. DOI： 10.11959/j.issn.1000-436x.2015017.

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。

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.

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