SCHEDULE: NOV 12-18, 2011

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A Framework for Data Intensive Computing with Cloud Bursting

SESSION: Research Poster Reception

EVENT TYPE: ACM Student Research Competition Poster, Poster, Electronic Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR: Bernd Mohr

AUTHOR(S):Tekin Bicer, David Chiu, Gagan Agrawal

ROOM:WSCC North Galleria 2nd/3rd Floors

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.

Chair/Author Details:

Bernd Mohr (Chair) - Juelich Supercomputing Centre

Tekin Bicer - Ohio State University

David Chiu - Washington State University

Gagan Agrawal - Ohio State University

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