TY - GEN
T1 - Performance analysis using petri net based mapreduce model in heterogeneous clusters
AU - Cheng, Sheng Tzong
AU - Wang, Hsi Chuan
AU - Chen, Yin Jun
AU - Chen, Chen Fei
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2015.
PY - 2015
Y1 - 2015
N2 - 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 finetune 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.
AB - 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 finetune 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.
UR - http://www.scopus.com/inward/record.url?scp=84961367848&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961367848&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-46315-4_18
DO - 10.1007/978-3-662-46315-4_18
M3 - Conference contribution
AN - SCOPUS:84961367848
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 170
EP - 179
BT - Advances in Web-Based Learning - ICWL 2013 Workshops - USL 2013, IWSLL 2013, KMEL 2013, IWCWL 2013, WIL 2013, and IWEEC 2013, Revised Selected Papers
A2 - Li, Qing
A2 - Lau, Rynson
A2 - Chiu, Dickson K.W.
A2 - Shih, Timothy K.
A2 - Yang, Chu-Sing
A2 - Popescu, Elvira
A2 - Wang, Minhong
A2 - Sampson, Demetrios G.
PB - Springer Verlag
T2 - 12th International Conference on Web-Based Learning, ICWL 2013, held with 1st International Workshop on Ubiquitous Social Learning, USL 2013, International Workshop on Smart Living and Learning, IWSLL 2013, International Workshop on Cloud Computing for Web-Based Learning, IWCWL 2013, International Workshop on Web Intelligence and Learning, WIL 2013, International Workshop on E-book and Education Cloud, IWEEC 2013 and 3rd International Symposium on Knowledge Management and E-Learning, KMEL 2013
Y2 - 6 October 2013 through 9 October 2013
ER -