Astronomical Time Series Data Analysis Leveraging Science Cloud
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.
KeywordsData-Intensive Computing Cloud Computing Virtual Infrastructure Astronomy Data Analysis
Unable to display preview. Download preview PDF.
- 1.Hey, T., Tansley, S., Tolle, K.: The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research (2009)Google Scholar
- 2.Sloan Digital Sky Survey Data Release 7, http://www.sdss.org/dr7/
- 3.LSST(Large Synoptic Survey Telescope) Data Management, http://www.lsst.org/lsst/science/concept_data
- 4.Square Kilometer Array, http://www.skatelescope.org
- 7.Ravichandran, D., Pantel, P., Hovy, E.: The Terascale Challenge. In: Proceedings of the KDD Workshop on Mining for and from the Semantic Web (2004)Google Scholar
- 8.Super WASP public data archive Data Release 1, http://www.wasp.le.ac.uk/public/
- 9.Condor Project, http://research.cs.wisc.edu/condor
- 10.OpenNebula, http://opennebula.org
- 11.Shin, M., Byun, Y., Chang, S., Kim, D., Kim, M., Lee, D., Hahm, J., Jung, Y., Yoon, Y.:Google Scholar
- 12.Kwak, J., Kim, J.H.: Detecting Variability in Astronomical Time Series Data. In: IAU Symposium, vol. 285 (2011)Google Scholar