Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (6): 1580-1586.DOI: 10.11772/j.issn.1001-9081.2017.06.1580

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Energy-efficient strategy for threshold control in big data stream computing environment

PU Yonglin1, YU Jiong1,2, WANG Yuefei2, LU Liang2, LIAO Bin3, HOU Dongxue1   

  1. 1. School of Software, Xinjiang University, Urumqi Xinjiang 830008, China;
    2. School of Information Science and Engineering, Xinjiang University, Urumqi Xinjiang 830046, China;
    3. School of Statistics and Information, Xinjiang University of Finance and Economics, Urumqi Xinjiang 830012, China
  • Received:2016-11-30 Revised:2017-01-17 Online:2017-06-10 Published:2017-06-14
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61462079,61562086,61562078), the Research Innovation Project of Graduate Student in Autonomous Region (XJGRI2016028).

大数据流式计算环境下的阈值调控节能策略

蒲勇霖1, 于炯1,2, 王跃飞2, 鲁亮2, 廖彬3, 侯冬雪1   

  1. 1. 新疆大学 软件学院, 乌鲁木齐 830008;
    2. 新疆大学 信息科学与工程学院, 乌鲁木齐 830046;
    3. 新疆财经大学 统计与信息学院, 乌鲁木齐 830012
  • 通讯作者: 于炯
  • 作者简介:蒲勇霖(1991-),男,山东淄博人,硕士研究生,CCF会员,主要研究方向:绿色计算、分布式计算;于炯(1964-),男,新疆乌鲁木齐人,教授,博士,CCF会员,主要研究方向:网格计算,绿色计算、分布式计算;王跃飞(1991-),男,新疆乌鲁木齐人,博士研究生,主要研究方向:分布式计算、网格计算;鲁亮(1990-),男,新疆乌鲁木齐人,博士研究生,CCF会员,主要研究方向:分布式计算、绿色计算、内存计算;廖彬(1986-),男,新疆乌鲁木齐人,CCF会员,副教授,博士,主要研究方向:绿色计算、数据库技术;侯冬雪(1992-),女,新疆昌吉人,硕士研究生,主要研究方向:推荐算法。
  • 基金资助:
    国家自然科学基金资助项目(61462079,61562086,61562078);自治区研究生科研创新项目(XJGRI2016028)。

Abstract: In the field of big data real-time analysis and computing, the importance of stream computing is constantly improved while the energy consumption of dealing with data on stream computing platform rises constantly. In order to solve the problems, an Energy-efficient Strategy for Threshold Control (ESTC) was proposed by changing the processing mode of node to data in stream computing. First of all, according to system load difference, the threshold of the work node was determined. Secondly, according to the threshold of the work node, the system data stream was randomly selected to determine the physical voltage of the adjustment system in different data processing situation. Finally, the system power was determined according to the different physical voltage. The experimental results and theoretical analysis show that in stream computing cluster consisting of 20 normal PCs, the system based on ESTC saves about 35.2% more energy than the original system. In addition, the ratio of performance and energy consumption under ESTC is 0.0803 tuple/(s·J), while the original system is 0.0698 tuple/(s·J). Therefore, the proposed ESTC can effectively reduce the energy consumption without affecting the system performance.

Key words: stream computing, threshold, load difference, random selection, system performance

摘要: 在大数据实时分析计算领域,流式计算的重要性不断提高,但是流式计算平台处理数据的能耗不断上升。针对这一问题,改变流式计算中节点对数据的处理方式,提出了一种阈值调控节能策略(ESTC)。首先,根据系统负载差异确定工作节点的阈值情况;其次,通过工作节点的阈值对系统数据流进行随机选择,确定不同数据处理情况调节系统的物理电压;最后,根据不同的物理电压确定系统功率。实验结果和理论分析表明,在20台普通PC机构成的流式计算集群中,实施ESTC的系统比原系统有效节能约35.2%;此外,ESTC下的性能与能耗的比值为0.0803 tuple/(s·J),而原系统性能与能耗的比值为0.0698 tuple/(s·J)。ESTC能够在不影响系统性能的前提下,有效降低了能耗。

关键词: 流式计算, 阈值, 负载差异, 随机选择, 系统性能

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