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The size of astronomical observational data is increasing yearly. Forexample, while Atacama Large Millimeter/submillimeter Array is expected togenerate 200 TB raw data every year, Large Synoptic Survey Telescope isestimated to produce 15 TB raw data every night. Since the increasing rate ofcomputing is much lower than that of astronomical data, to provide highperformance computing (HPC) resources together with scientific data will becommon in the next decade. However, the installation and maintenance costs of aHPC system can be burdensome for the provider. I note public cloud computingfor an alternative way to get sufficient computing resources inexpensively. Ibuild Hadoop and Hive clusters by utilizing a virtual private server (VPS)service and Amazon Elastic MapReduce (EMR), and measure their performances. TheVPS cluster behaves differently day by day, while the EMR clusters arerelatively stable. Since partitioning is essential for Hive, severalpartitioning algorithms are evaluated. In this paper, I report the results ofthe benchmarks and the performance optimizations in cloud computingenvironment.

Pre-feasibility Study of Astronomical Data Archive Systems Powered by Public Cloud Computing and Hadoop Hive