TY - GEN
T1 - Speed-based load balancer for scheduling reduce tasks to process intermediate data of MapReduce applications on cloud computing
AU - Huang, Tzu Chi
AU - Chu, Kuo Chih
AU - Shieh, Ce Kuen
AU - Tsai, Ming Fong
PY - 2015/10/7
Y1 - 2015/10/7
N2 - MapReduce is a programming model used to develop applications on cloud computing. However, MapReduce strongly relies on the runtime system to handle issues of task managements in order to get a better performance. While most research works focus on scheduling Map tasks to improve performances, MapReduce is identified by this paper to have the potential for performance improvements through the Speed-based Load Balancer (SLB) for scheduling Reduce tasks. According to observations on experiments of Inverted Index, Radix Sort and Word Count, MapReduce can use SLB to outperform the native scheduler used by Hadoop in the runtime system.
AB - MapReduce is a programming model used to develop applications on cloud computing. However, MapReduce strongly relies on the runtime system to handle issues of task managements in order to get a better performance. While most research works focus on scheduling Map tasks to improve performances, MapReduce is identified by this paper to have the potential for performance improvements through the Speed-based Load Balancer (SLB) for scheduling Reduce tasks. According to observations on experiments of Inverted Index, Radix Sort and Word Count, MapReduce can use SLB to outperform the native scheduler used by Hadoop in the runtime system.
UR - http://www.scopus.com/inward/record.url?scp=84959934545&partnerID=8YFLogxK
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U2 - 10.1145/2818869.2818880
DO - 10.1145/2818869.2818880
M3 - Conference contribution
AN - SCOPUS:84959934545
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the ASE BigData and SocialInformatics 2015, ASE BD and SI 2015
PB - Association for Computing Machinery
T2 - ASE BigData and SocialInformatics, ASE BD and SI 2015
Y2 - 7 October 2015 through 9 October 2015
ER -