期刊论文

Wang, Lizhe

英文

Future Generation Computer Systems

0167-739X

2013

29

3

739-750

10.1016/j.future.2012.09.001

Chinese Academy of SciencesChinese Academy of Sciences; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61272314]; Natural Science Foundation of Hubei Province of ChinaNatural Science Foundation of Hubei Province [2011CDB159]; Program for New Century Excellent Talents in UniversityProgram for New Century Excellent Talents in University (NCET) [NCET-11-0722]; Specialized Research Fund for the Doctoral Program of Higher EducationSpecialized Research Fund for the Doctoral Program of Higher Education (SRFDP) [20110145110010]; Fundamental Research Funds for the Central Universities (CUG, Wuhan)

本校为其他机构

Recently, the computational requirements for large-scale data-intensive analysis of scientific data have grown significantly. In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. This huge amount of data is processed on more than 140 computing centers distributed across 34 countries. The MapReduce paradigm has emerged as a highly successful programming model for large-scale data-intensive computing applications. However, current MapReduce implementations are developed to operate on single cluster environments and cannot be leveraged f...