期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

云计算环境下改进的能效度量模型 被引量：9

Improved energy-efficiency measurement model for cloud computing
下载PDF
导出
摘要 针对大规模计算的能效问题,提出改进的能效度量模型,通过"能源"和"效率"2种度量来综合评价系统能效.在"能源"方面,考虑计算机、网络和附属设备的能耗;在"效率"方面,考虑CPU、内存、磁盘以及网络的情况.提出的能效度量模型描述了改进后的能效度量的定义和数学表达,通过实验验证了该模型的合理性.基于该度量模型,评估并分析了MapReduce环境中CPU密集型、I/O密集型和交互型计算的能效,总结了MapReduce环境中的能效规律. An energy efficiency measurement model was proposed to address the large-scale computing problem through two metrics： ＂energy＂ and ＂efficiency＂. As for ＂energy＂, the energy consumption of computer, network and affiliated equipments was considered; as for ＂efficiency＂, that of CPU, memory, disk and network was considered. The proposed energy efficiency measurement model describes the defini- tion and mathematical expression of the improved energy efficiency measurement, and is proved reasonable through experiments. The energy efficiency of CPU intensive, I/O intensive and interactive computing was evaluated and analyzed based on the measurement model, and the energy efficiency laws in MapReduce environment were summarized.
作者 宋杰 侯泓颖 王智 朱志良
机构地区 东北大学软件学院
出处 《浙江大学学报（工学版）》 EI CAS CSCD 北大核心 2013年第1期44-52,共9页 Journal of Zhejiang University：Engineering Science
基金 国家自然科学基金资助项目(61202088) 辽宁省自然科学基金资助项目(200102059) 中央高校基本科研业务费专项资金资助项目(N110417002)
关键词 云计算 能效 度量模型 MAPREDUCE cloud computing； energy efficiency； measurement model； MapReduce
分类号 TP301.41 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献10

  • 1Gartner Incorporation. Gartner says worldwide cloud serv- ices revenue will grow 21.3 percent in 2009 [EB/OL]. [2012-02- 01]. http;//www, gartner, corn/it/page, jsp? id =920712.
  • 2宋杰,李甜甜,闫振兴,那俊,朱志良.一种云计算环境下的能效模型和度量方法[J].软件学报,2012,23(2):200-214. 被引量：70
  • 3XU Zi-chen, TU Yi-Cheng, WANG Xiao rui. Exploring power-performance tradeoffs in database systems [C]// ICDE 2010. California:IEEE, 2010:485 - 496.
  • 4TSIROGIANNIS D, HARIZOPOULOS S, SHAH M A. Analyzing the energy efficiency of a database server [C] // SIGMOD 2010. Indianapolis, Indiana, USA: ACM, 2010: 231-242.
  • 5BARROSO L A, HOLZLE U. The case for energy-pro- portional computing computer [J]. IEEE Computer, 2007, 40(12): 33-37.
  • 6KUMAR K, I.U Y H. Cloud computing for mobile us ers: can offloading computation save energy? [J]. IEEE Computer, 2010, 43(4) : 51 - 56.
  • 7ORGERIE A C, EVRE L L, GELAS J P. Save Watts in your grid= green strategies for energy-aware frame work in large scale distributed systems [C]// ICPADS 2008. Melbourne: IEEE, 2008:171-178.
  • 8YOUNGE A J, VON LASZEWSKI G, WANG L, et al. Efficient resource management for cloud computing environments [C]// International Green Computing Con- ference. Chicago: IEEE, 2010: 357-364.
  • 9SRIKANTAIAH S, KANSAL A, ZHAO F. Energy a- ware consolidation for cloud computing [C]// Confer- ence on Power Aware Computing and Systems. Berkeley: USENIX Association, 2008: 10.
  • 10ABDELSALAM H S, MALY K, MUKKAMALA R, et al. Analysis of energy efficiency in clouds [C] // Fu- ture Computing, Service Computation, Cognitive, Adap- tive, Content, Patterns, Computation World. Norfolk: IEEE, 2009:416-421.

二级参考文献27

  • 1Chert G, He WB, Liu J, Nath S, Rigas L, Xiao L, Zhao F. Energy-Aware server provisioning and load dispatching for connection- intensive Internet services. In: Crowcroft J, Dahlin M, eds. Proc. of the 5th USENIX Syrup. on Networked Systems Design and Implementation (NSDI). San Francisco: USENIX Association, 2008. 337-350.
  • 2Urgaonkar B, Shenoy PJ, Chandra A, Goyal P, Wood T. Agile dynamic provisioning of multi-tier Internet applications. Trans. on Autonomous and Adaptive Systems, 2008,3(1):1-39. [doi: 10.1145/1342171.1342172].
  • 3Orgerie AC, Lef~vre L, Gelas JP. Save Watts in your grid: Green strategies for energy-aware framework in large scale distributed systems. In: Proc. of the 14th Int'l Conf. on Parallel and Distributed Systems (ICPADS 2008), Melbourne: IEEE, 2008. 171-178. Idol: 10.1109/ICPADS.2008.97].
  • 4IBM proj oct big green, http://www-03.ibm.com/press/us/en/pressrelease/21524.wss.
  • 5Using virtualization to improve data center efficiency, http://www.thegreengrid.org/Global/Content/white-papers/Using- Virtualization-to-Improve-Data-Center-Efficiency.
  • 6Rivoire S, Shah MA, Ranganathan P, Kozyrakis C. JouleSort: A balanced energy-efficiency benchmark. In: Chan CY, Qoi BC, Zhou A, eds. Prec. of the ACM SIGMOD Int'l Conf. on Management of Data. B~ijing: ACM Press, 2007. 365-376. Idol: 10.1145/ 1247480.1247522].
  • 7Bahsoon R. Green cloud: Towards a framework for dynamic self-optimization of power and dependability requirements in green cloud architectures. In: Babar MA, Gorton I, eds. Proe. of the 4th European Conf. on Software Architecture (ECSA 2010). Copenhagen, 2010. 510-514.
  • 8Kumar K, Lu YH. Cloud computing for mobile users: Can offloading computation save energy? IEEE Computer, 2010,43(4): 51-56. [doi: 10.1109/MC.2010.98].
  • 9Kelenyi I, Nurminen JK. CloudTorrent--Energy-Efficient BitTorrent content sharing for mobile devices via cloud services. In: Proc. of the 7th IEEE on Consumer Communications and Networking Conf. (CCNC). 2010. 1-2.
  • 10Elnozahy EN, Kistler M, Rajamony R. Energy-Efficient server clusters. In: Falsafi B, Vijaykumar TN, eds. Proc. of the 2nd Int'l Workshop on Power-Aware Computer Systems (PACS 2002). Cambridge: Springer-Verlag, 2003. 179-197. [doi: 10.1007/3-540- 36612-1_12].

共引文献69

同被引文献80

  • 1HAN Cong-zheng,HARROLD T,ARMOUR S, et al. Green radio: radio techniques to enable energy efficient wireless networks [ J ]. IEEE Communications Magazine: Special Issue on Green Ra- dio Communications, 2010,49(6) :46-55.
  • 2McLAUGHLIN S, GRANT P M, THOMPSON J S, et al. Techniques for improving cellular radio base station energy efficiency[ J]. IEEE Communications Magazine, 2011,18 ( 5 ) : 10-17.
  • 3HAN Tao, ANSARI N. ICE: intelligent cell BrEathing to optimize the utilization of green energy[ J ]. IEEE Communications Letters, 2012,16(6) : 866-869.
  • 4CHEN Hou-chun, FU Huai-lei, LIN P, et al. Energy-Aware trans- mission scheduling in mobile sensor networks [ C]//Proc of IEEE Global Telecommunications Conference. 2011 : 1-5.
  • 5Gaeke BR, Husbands P, Li XS, et al. Memory-intensive benchmarks: IRAM vs. cache-based machines. Proc. International Parallel and Distributed Processing Symposium. IPDPS 2002, Abstracts and CD-ROM. IEEE, 2001.
  • 6Chase JS, Anderson DC, Thakar PN, et al. Managing energy and server resources in hosting centers. ACM SIGOPS Operating Systems Review, ACM, 2001, 35(5): 103-116.
  • 7Snowdon DC, Ruocco S, Heiser G; Power management and dynamic voltage scaling: Myths and facts. 2005.
  • 8Kappiah N, Freeh VW, Lowenthal DK. Just in time dynamic voltage scaling: Exploiting inter-node slack to save energy in MPI programs. Proc. of the 2005 ACM/IEEE Conference on Supercomputing. IEEE Computer Society. 2005.33.
  • 9Ecolnfo 2011. From windows 95 to windows 7. Ecolnfo report.
  • 10Carrera EV, Pinheiro E, Bianchini R. Conserving disk energy in network servers. Proc. of the 17th Annual International Conference on Supercomputing. ACM. 2003.86-97.

引证文献9

二级引证文献59

浙江大学学报（工学版）
2013年 第1期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部