TY - GEN
T1 - MapReduce application profiler
AU - Huang, Tzu Chi
AU - Chu, Kuo Chih
AU - Chiu, Chui Ming
AU - Shieh, Ce-Kuen
PY - 2014/1/1
Y1 - 2014/1/1
N2 - MapReduce is a programming model popularized by Google to process large data in clusters and has become a key technology on cloud computing nowadays. Due to the feature of simplicity, MapReduce attracts many application developers to develop related applications. However, MapReduce currently has few solutions to help application developers with the tasks of profiling their applications. In this paper, MapReduce Application Profiler (MRAP) is proposed to facilitate profiling MapReduce applications in clusters.
AB - MapReduce is a programming model popularized by Google to process large data in clusters and has become a key technology on cloud computing nowadays. Due to the feature of simplicity, MapReduce attracts many application developers to develop related applications. However, MapReduce currently has few solutions to help application developers with the tasks of profiling their applications. In this paper, MapReduce Application Profiler (MRAP) is proposed to facilitate profiling MapReduce applications in clusters.
UR - http://www.scopus.com/inward/record.url?scp=84958525423&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958525423&partnerID=8YFLogxK
U2 - 10.1007/978-94-007-7262-5_84
DO - 10.1007/978-94-007-7262-5_84
M3 - Conference contribution
AN - SCOPUS:84958525423
SN - 9789400772618
T3 - Lecture Notes in Electrical Engineering
SP - 741
EP - 749
BT - Advanced Technologies, Embedded and Multimedia for Human-Centric Computing, HumanCom and EMC 2013
PB - Springer Verlag
T2 - Advanced Technologies, Embedded and Multimedia for Human-Centric Computing, HumanCom and EMC 2013
Y2 - 23 August 2013 through 25 August 2013
ER -