DocumentCode :
710417
Title :
Performance modelling and analysis of mapreduce/hadoop workloads
Author :
Xiaolong Yu ; Wei Li
Author_Institution :
Sch. of Comput. Sci. & Technol., ShanDong Univ., Jinan, China
fYear :
2015
fDate :
22-24 April 2015
Firstpage :
1
Lastpage :
6
Abstract :
Data center is the infrastructure in big data processing, which constructs computing platform by distributed computer. The paper aims to investigate the analytical model by adopting queueing theory in data center of big data. The new queueing model developed fits the MapReduce programming model accurately and discovers the nature of the programming model. The utilizations and mean waiting times of Mapper and Reducer are obtained respectively. The effect of workload (and number of Mapper slots) on the system performance (i.e., utilization) is exposed. The significance of this paper is it explores the theoretical insight of the MapReduce programming model and provides the optimal parameter recommendation for computing resource configuration.
Keywords :
Big Data; computer centres; parallel programming; queueing theory; resource allocation; MapReduce programming model; MapReduce-Hadoop workloads; Mapper slots; Reducer; big data processing; computing resource configuration; data center; distributed computer; optimal parameter recommendation; queueing theory; system performance; Analytical models; Computational modeling; Load modeling; Processor scheduling; Programming profession; Queueing analysis; Analytical models; Big data; MapReduce; Queueing network; Workloads;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Local and Metropolitan Area Networks (LANMAN), 2015 IEEE International Workshop on
Conference_Location :
Beijing
Type :
conf
DOI :
10.1109/LANMAN.2015.7114723
Filename :
7114723
Link To Document :
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