University of Wisconsin, Madison
The team at the University of Wisconsin Madison have designed the Hierarchically-Redundant, Decoupled storage project (HaRD) to investigate the next generation of storage software for hybrid Flash/disk storage clusters. They are also working with MIT and Yale University on a comparative study, funded by a National Science Foundation (NSF) CLuE grant, of approaches to cluster-based, large-scale data analysis.
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A Comparative Study of Approaches to Cluster-Based Large Scale Data Analysis |
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This is a collaborative study being conducted by MIT, University of Wisconsin, and Yale University. These three universities are using a National Science Foundation CLuE grants for a comparative study of approaches to cluster-based, large-scale data analysis. Both MapReduce and parallel database systems provide scalable data processing over hundreds to thousands of nodes, yet it's important for researchers to know the differences in performance and scalability of these two approaches to know which is more suitable when designing new data-intensive computing applications.
This project is engaged in systems research, much of which requires the ability to change the operating environment. Since this is not possible on the IBM/Google cluster, the project is also hosted on the Cloud Computi ....
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Hierarchically-Redundant, Decoupled Storage Project (HaRD) |
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The Wisconsin Hierarchically-Redundant, Decoupled storage project (HaRD) investigates the next generation of storage software for hybrid Flash/disk storage clusters. The main objective of the project is to improve the performance of storage in a variety of diverse scenarios, including new application environments such as photo storage as found in Facebook and Flickr, high-end scientific processing as found in government labs, and large-scale data processing such as that found in Google and Microsoft.
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