論文

査読有り
2015年6月

Long-term Performance Evaluation of Hadoop Jobs in Public and Community Clouds

IEICE Transactions on Information and Systems
  • Kento Aida
  • ,
  • Omar Abdul-Rahman
  • ,
  • Eisaku Sakane
  • ,
  • Kazutaka Motoyama

E98-D
6
開始ページ
1176
終了ページ
1184
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1587/transinf.2014EDP7274
出版者・発行元
The Institute of Electronics, Information and Communication Engineers

Cloud computing is a widely used computing platform in business and academic communities. Performance is an important issue when a user runs an application in the cloud. The user may want to estimate the application-execution time beforehand to guarantee the application performance or to choose the most suitable cloud. Moreover, the cloud system architect and the designer need to understand the application performance characteristics, such as the scalability or the utilization of cloud platforms, to improve performance. However, because the application performance in clouds sometime fluctuates, estimation of the application performance is difficult. In this paper, we discuss the performance fluctuation of Hadoop jobs in both a public cloud and a community cloud for one to three months. The experimental results indicate phenomena that we cannot see without long-term experiments and phenomena inherent in Hadoop. The results suggest better ways to estimate Hadoop application performances in clouds. For example, we should be aware of application characteristics (CPU intensive or communication intensive), datacenter characteristics (busy or not), and time frame (time of day and day of the week) to estimate the performance fluctuation due to workload congestion in cloud platforms. Furthermore, we should be aware of performance degradation due to task re-execution in Hadoop applications.

リンク情報
DOI
https://doi.org/10.1587/transinf.2014EDP7274
CiNii Articles
http://ci.nii.ac.jp/naid/130005072402
URL
https://jlc.jst.go.jp/DN/JLC/20011223755?from=CiNii
ID情報
  • DOI : 10.1587/transinf.2014EDP7274
  • ISSN : 0916-8532
  • CiNii Articles ID : 130005072402

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