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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationEmbedded and Multimedia Computing Technology and Service, EMC 2012
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9789400750753
Publication statusPublished - 2012
Event7th International Conference on Embedded and Multimedia Computing, EMC 2012 - Gwangju, Korea, Republic of
Duration: 2012 Sept 62012 Sept 8

Publication series

NameLecture Notes in Electrical Engineering
Volume181 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


Other7th International Conference on Embedded and Multimedia Computing, EMC 2012
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering


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