• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.75-88
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• 2004

Condor is a distributed batch system for sharing the workload among the computers connected by a network. Condor distributed system was developed on the basis that every machine in a Condor pool is always connected by a network to run a Condor Job. Due to advances in wireless communication and mobile computing technology, conventional distributed computer systems can now include “mobile” clients as well as “fixed” clients. Moreover, mobile users want to lower their power consumption by off-loading potentially power and resource consuming jobs. In this paper, we describe the design and implementation of mobile interface for mobile clients in Condor distributed batch system. The main purpose of this work is to enable users on mobile computers to interact with Condor environment any time anywhere regardless of their connection to a Condor pool. The mobile Condor distributed system also aims to provide mobile users the same Condor services without making any significant changes to the existing Condor system.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.131.1-131.1
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• 2011

We present applications of clustering methods to detect variability in massive astronomical time series data. Focusing on variability of bright stars, we use clustering methods to separate possible variable sources from other time series data, which include intrinsically non-variable sources and data with common systematic patterns. We already finished the analysis of the Northern Sky Variability Survey data, which include about 16 million light curves, and present candidate variable sources with their association to other data at different wavelengths. We also apply our clustering method to the light curves of bright objects in the SuperWASP Data Release 1. For the analysis of the SuperWASP data, we exploit a elastically configurable Cloud computing environments that the KISTI Supercomputing Center is deploying. Two quite different configurations are incorporated in our Cloud computing test bed. One system uses the Hadoop distributed processing with its distributed file system, using distributed processing with data locality condition. Another one adopts the Condor and the Lustre network file system. We present test results, considering performance of processing a large number of light curves, and finding clusters of variable and non-variable objects.