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

基于对象化并行计算复杂场景的解决方案

Objectification parallel computing of research in grid assets
下载PDF
导出
摘要 由于当前电力行业业务处理复杂,读取吞吐量大,业务数据海量等特点,传统计算方法及传统数据库交互方式,已无法满足用户的效率需求。提出了一种高性能对象化并行计算框架,该框架由内存服务器,内存管理服务器,客户端三个组件构成,融合了对象化技术,内存计算,分布式计算等思想,并且在国家电网电能质量在线监测系统中得到了应用。结果表明该框架能够大幅度提升数据的处理效率。 Nowadays,because of the complex transaction processing,the large data throughput and the massive business data in electric power big data,traditional computing methods and traditional database have not been satisfied users ' demands. The paper provides the objectification parallel computing architecture in order to deal with this problem. The architecture includes the object manager server,the object server and client proxy and it mixed objectification,memory computing and parallel computing.This system is applied to the Electric Asset Quality Supervision Manager System of State Grid Of China.The result shows that the system improves the performance significantly.
作者 刘铭 张春平 杨志 张琦
机构地区 国网电力科学研究院
出处 《信息技术》 2016年第9期152-155,160,共5页 Information Technology
关键词 复杂场景 分布式计算 内存计算 并行计算 对象化 complex scene distributed computing memory computing parallel computing objectification
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献16

二级参考文献375

  • 1何克清,何非,李兵,何扬帆,刘进,梁鹏,王翀.面向服务的本体元建模理论与方法研究[J].计算机学报,2005,28(4):524-533. 被引量：36
  • 2Zhou MQ, Zhang R, Zeng DD, Qian WN, Zhou AY. Join optimization in the MapReduce environment for column-wise data store. In: Fang YF, Huang ZX, eds. Proc. of the SKG. Ningbo: IEEE Computer Society, 2010.97-104. [doi: 10.1109/SKG.2010.18].
  • 3Afrati FN, Ullman JD. Optimizing joins in a Map-Reduce environment. In: Manolescu I, Spaecapietra S, Teubner J, Kitsuregawa M, Leger A, Naumann F, Ailamaki A, Ozcan F, eds. Proc. of the EDBT. Lausanne: ACM Press, 2010. 99-110. [doi: 10.1145/ 1739041.1739056].
  • 4Sandholm T, Lai K. MapReduce optimization using regulated dynamic prioritization. In: Douceur JR, Greenberg AG, Bonald T, Nieh J, eds. Proc. of the SIGMETRICS. Seattle: ACM Press, 2009. 299-310. [doi: 10.1145/1555349.1555384].
  • 5Hoefler T, Lumsdaine A, Dongarra J. Towards; efficient MapReduce using MPI. In: Oster P, ed. Proc. of the EuroPVM/MPI. Berlin: Springer-Verlag, 2009. 240-249. [doi: 10.100'7/978-3-642-03770-2_30].
  • 6Nykiel T, Potamias M, Mishra C, Kollios G, Koudas N. MRShare: Sharing across multiple queries in MapReduce. PVLDB, 2010, 3(1-2):494-505.
  • 7Kambatla K, Rapolu N, Jagannathan S, Grama A. Asynchronous algorithms in MapReduce. In: Moreira JE, Matsuoka S, Pakin S, Cortes T, eds. Proc. of the CLUSTER. Crete: IEEE Press, 2010. 245-254. [doi: 10.1109/CLUSTER.2010.30].
  • 8Polo J, Carrera D, Becerra Y, Torres J, Ayguad6 E, Steinder M, Whalley I. Performance-Driven task co-scheduling for MapReduce environments. In: Tonouchi T, Kim MS, eds. Proc. of the 1EEE Network Operations and Management Symp. (NOMS). Osaka: IEEE Press, 2010. 373-380. [doi: 10.1109/NOMS.2010.5488494].
  • 9Zaharia M, Konwinski A, Joseph AD, Katz R, Stoica I. Improving MapReduce performance in heterogeneous environments. In: Draves R, van Renesse R, eds. Proc. of the ODSI. Berkeley: USENIX Association, 2008.29-42.
  • 10Xie J, Yin S, Ruan XJ, Ding ZY, Tian Y, Majors J, Manzanares A, Qin X. Improving MapReduce performance through data placement in heterogeneous Hadoop clusters. In: Taufer M, Rfinger G, Du ZH, eds. Proc. of the Workshop on Heterogeneity in Computing (IPDPS 2010). Atlanta: IEEE Press, 2010. 1-9. [doi: 10.1109/IPDPSW.2010.5470880].

共引文献4735

信息技术
2016年 第9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