Paper Info
Title
Performance Analysis Using Petri Net Based MapReduce Model in Heterogeneous Clusters.
Abstract
Currently, big data and large-scale data processing techniques has become an important developing area. MapReduce is an enabling technology of cloud computing. Hadoop is one of the most popular MapReduce implementation, which is the target platform in this paper. When running a MapReduce job, programmers however cannot acquire the information about how to fine-tune the parameters of application. Moreover, programmers need much time on finding the most suitable parameters. This paper evaluates execution processes in MapReduce and form SPN-MR model with Stochastic Petri Net. In order to analyze the performance of SPN-MR, formulas of mean delay time in each time transition are defined. SPN-MR simulates the elapsed time of any MapReduce jobs with known input data sizes and then reduces time cost in performance tuning. SPN-MR carried out several actual test benchmarks. The results showed the average error rate is within 5 percent. Therefore, it can provide effective performance evaluation reports for MapReduce programmers.
Year
DOI
Venue
2013
10.1007/978-3-662-46315-4_18
ADVANCES IN WEB-BASED LEARNING
Keywords
Field
DocType
Cloud computing,MapReduce,Petri net,Performance analysis
Cluster (physics),Data processing,Petri net,Computer science,Word error rate,Stochastic Petri net,Performance tuning,Big data,Cloud computing,Distributed computing
Conference
Volume
ISSN
Citations 
8390
0302-9743
0
PageRank 
References 
Authors
0.34
11
4
Name
Order
Citations
PageRank
Sheng-Tzong Cheng129344.23
Hsi-Chuan Wang200.34
Yin-Jun Chen3164.11
Chen-Fei Chen400.34