标题:Energy-optimization scheduling of task dependent graph on DVS-enabled cluster system
作者:Ma, Yan ;Gong, Bin ;Zou, Lida
通讯作者:Ma, Y
作者机构:[Ma, Yan ;Gong, Bin ] School of Computer Science and Technology, Shandong University, Jinan, Shandong, China;[Zou, Lida ] Information Technology Schoo 更多
会议名称:5th Annual ChinaGrid Conference, ChinaGrid 2010
会议日期:16 July 2010 through 18 July 2010
来源:Proceedings - 5th Annual ChinaGrid Conference, ChinaGrid 2010
出版年:2010
页码:183-190
DOI:10.1109/ChinaGrid.2010.16
关键词:Energy-aware scheduling; High performance computing; Power management
摘要:At present, power management in High-Performance Computing (HPC) environment is becoming a hot topic owning to its high operation cost, low reliability and environmental impact. In this paper, we investigate energy minimization scheduling algorithm of data dependent tasks in DVS-Enabled cluster system. Considering the data-intensive characteristics, the proposed EOTD (Energy Optimization scheduling for Task Dependent graph) algorithm adopts task clustering to reduce data transmission time and communication energy consumption. In order to decrease dynamic power of processing elements, it uses one of the power-saving techniques in system level- Dynamic Voltage Scaling while not violating the deadline users specify. Moreover, on the premise that application execution is predictive and exclusive for processing elements, we employ Dynamic Power Management and Binary Search technique to reduce the static power of idle processing elements and last find the optimal number of processing elements. EOTD algorithm not only optimizes the energy consumption of task dependent graph, but also satisfies the QoS requirements service level agreement gives. Compared with VM and LJ-VM algorithm, experimental results demonstrate that EOTD algorithm can achieve larger energy optimization in less optimizing time. © 2010 IEEE.
收录类别:EI;SCOPUS
Scopus被引频次:9
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-77958160025&doi=10.1109%2fChinaGrid.2010.16&partnerID=40&md5=0f8ef47ee332cf4b6d8ed06630c134fd
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