期刊文献+

基于集群规模调整的节能存储策略 被引量:1

Energy-conserving strategies of file storage based on cluster scale adjustment
下载PDF
导出
摘要 根据谷歌数据中心研究报告,传统数据中心存在高能耗、低利用率的问题。通过研究集群数据块访问规律,提出一种基于集群规模调整的Hadoop分布式文件系统(HDFS)节能存储策略,实现HDFS高效节能存储。策略主要在集群区域划分、数据块迁移策略优化、缓存机制等方面作出了改进。实验结果表明:使用该节能策略的HDFS比传统HDFS节能35%~40%,其中0.3%的访问需要唤醒服务器,同时引入缓存策略对集群的性能提高了5.1%。 According to the research report of the google data-center,traditional data-center has the problem of the high energy consumption and low utilization ratio.A Hadoop Distributed File System(HDFS)energy-saving storage scheme based on cluster scale adjustment is proposed to realize HDFS efficient energy-saving storage by the access rules of cluster data block in this paper.The strategy is improved in the cluster in the regional division,data block migration strategy optimization and caching mechanisms.The simulation experiment results show that using the energy-saving strategies of HDFS energy-saving varies between35%~40%than traditional HDFS,0.3%access need to wake up the servers.At the same time,while the caching storage is introducted,the performance of the cluster is improved by5.1%compared with traditional HDFS.
作者 妙晓龙 陈浩 钟将 MIAO Xiaolong;CHEN Hao;ZHONG Jiang(College of Computer Science, Chongqing University, Chongqing 400044, China;Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400044, China)
出处 《计算机工程与应用》 CSCD 北大核心 2017年第24期80-85,共6页 Computer Engineering and Applications
基金 国家高技术研究发展计划(863)(No.2015AA015308) 中央高校业务基金项目(No.106112014CDJZR188801) 国家自然科学基金(No.61402020) 重庆市教育委员会重庆市研究生教育优质课程(No.201507)
关键词 HADOOP HADOOP分布式文件系统 节能 规模调整 缓存 Hadoop Hadoop Distributed File System(HDFS) energy-conservation scale adjustment cache
  • 相关文献

参考文献1

二级参考文献37

  • 1Yun D, Lee J. Research in green network for future Inter- net. Journal of KIISE, 2010, 28(1): 41-51.
  • 2Barroso L A, Holzle U. The case for energy-proportional computing. Computer, 2007, 40(12):33-37.
  • 3Ghemawat S, Gobioff H, Leung ST. The Google File Sys tem//Proceedings of the 19th ACM Symposium on Operating System Principles (SOSP2003). New York, USA, 2003: 29-43.
  • 4Dean J, Ghemawat S. MapReduce: Simplifed data processing on large clusters//Proceedings of the Conference on Operat- ing System Design and Implementation (OSDI). San Francis- co, USA, 2004: 137-150.
  • 5Chang F, Dean J, Ghemawat S, et al. Bigtable: A distribu- ted storage system for structured data//Proceedings of the 7th Symposium on Operating Systems Design and Implemen- tation (OSDI). Seattle, USA, 2006:205-218.
  • 6Benini L, Bogliolo A, Mieheli G D. A survey of design tech- niques for system level dynamic power management. IEEE Transactions on Very Large Scale Integration (VLSI) Sys- tems, 2000, 8(3): 299 -316.
  • 7Albers S. Energy efficient algorithms. Communications of the ACM, 2010, 53(5): 86-96.
  • 8Srivastava M B, Chandrakasan A P, Brodersen R W. Predic- tive system shutdown and other architectural techniques for energy efficient programmable computation. IEEE Transac- tions on Very Large Scale Integration (VLSI) Systems, 1996, 4(1): 42-55.
  • 9Hwang C H, Wu A C. A predictive system shutdown meth- od for energy saving of event-driven computation. ACM Transactions on Design Automation of Electronic Systems (TODAES), 2000, 5(2) : 241-246.
  • 10Wierman A, Andrew L L, Tang A. Power-aware speed scal- ing in processor sharing systems//Proceedings of the 28th Conference on Computer Communications ( INFOCOM 2009). Rio, Brazil, 2009: 2007-2015.

共引文献57

同被引文献15

引证文献1

二级引证文献4

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