Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

22

IPPS
2010
IEEE

favoriteEmaildiscussreport

335views Distributed And Parallel Com...» more IPPS 2010»

Improving MapReduce performance through data placement in heterogeneous Hadoop clusters

13 years 2 months ago

Improving MapReduce performance through data placement in heterogeneous Hadoop clusters

Download www.eng.auburn.edu

MapReduce has become an important distributed processing model for large-scale data-intensive applications like data mining and web indexing. Hadoop

Jiong Xie, Shu Yin, Xiaojun Ruan, Zhiyang Ding, Yu

Real-time Traffic

Data Locality | Data Placement | Data-intensive Application | Distributed And Parallel Computing | IPPS 2010 |

claim paper

Related Content

» Improving MapReduce Performance in Heterogeneous Environments

» Improving the Hadoop mapreduce framework to support concurrent appends through the BlobSee...

» Clydesdale structured data processing on MapReduce

» The Performance of MapReduce An Indepth Study

» Enhancing MapReduce via Asynchronous Data Processing

» Hadoop Making a Yellow Elephant Run Like a Cheetah Without It Even Noticing

» MITHRA Multiple data independent tasks on a heterogeneous resource architecture

» Cluster computing for webscale data processing

» Analyzing massive astrophysical datasets Can PigHadoop or a relational DBMS help

Post Info
More Details (n/a)

Added	13 Feb 2011
Updated	13 Feb 2011
Type	Journal
Year	2010
Where	IPPS
Authors	Jiong Xie, Shu Yin, Xiaojun Ruan, Zhiyang Ding, Yun Tian, James Majors, Adam Manzanares, Xiao Qin

Comments (0)