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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
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DTSTART:20111116T011500Z
DTEND:20111116T030000Z
LOCATION:WSCC North Galleria 2nd/3rd Floors
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: In this poster, we consider the challenge of data analysis in a scenario where data is stored across a local cluster and cloud resources. We describe a software framework to enable data-intensive computing with cloud bursting, i.e., using a combination of compute resources from a local cluster and a cloud environment to perform Map-Reduce type processing on a data set that is geographically distributed. Our evaluation with three different applications shows that data-intensive computing with cloud bursting is feasible and scalable. As compared to a situation where the data set is stored at one location and processed using resources at that end, the average slowdown of our system is only 15.55%. Thus, the overheads due to global reduction, remote data retrieval, and potential load imbalance are quite manageable. Our system scales with an average speedup of 81% when the number of compute resources is doubled.
SUMMARY:A Framework for Data Intensive Computing with Cloud Bursting
PRIORITY:3
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