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Astronomical Time Series Data Analysis Leveraging Science Cloud

  • Jaegyoon Hahm
  • Oh-Kyoung Kwon
  • Sangwan Kim
  • Yong-Hwan Jung
  • Joon-Weon Yoon
  • Joo Hyun Kim
  • Mi-Kyoung Kim
  • Yong-Ik Byun
  • Min-Su Shin
  • Chanyeol Park
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 181)

Abstract

The volume of datasets to be handled by scientific applications is increasing abruptly. Data-intensive sciences challenged by the big data problems need more elastic and scalable computing infrastructure than traditional infrastructure adhesive to compute-intensive computing applications. Cloud computing is rising alternative to existing compute-intensive high performance computing infrastructures. In this work we present an astronomical time series data analysis on cloud computing as a typical data-intensive scientific application. We implemented a private IaaS cloud which is virtual resource provision service to data analysis applications. We utilize OpenNebula as a virtual machine man- ager and implemented virtual cluster service which gives virtual private cluster instances based on user demand. Detecting variable bright stars from SuperWASP time series data is successfully done in our virtual clusters, which shows the viability of cloud computing for data-intensive sciences.

Keywords

Data-Intensive Computing Cloud Computing Virtual Infrastructure Astronomy Data Analysis 

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Jaegyoon Hahm
    • 1
  • Oh-Kyoung Kwon
    • 1
  • Sangwan Kim
    • 1
  • Yong-Hwan Jung
    • 1
  • Joon-Weon Yoon
    • 1
  • Joo Hyun Kim
    • 1
  • Mi-Kyoung Kim
    • 1
  • Yong-Ik Byun
    • 2
  • Min-Su Shin
    • 3
  • Chanyeol Park
    • 1
  1. 1.Korea Institute of Science and Technology InformationDaejeonKorea
  2. 2.Yonsei UniversitySeoulKorea
  3. 3.University of MichiganAnn ArborUSA

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