Drs. Wu Feng, Heshan Lin and Joao Setubal Win NSF Award for Cloud Computing

Publish Date: 04/30/2011

Congratulations to Drs. Wu Feng, Heshan Lin and João Setubal on their new NSF Award entitled "CiC (RDDC): Commoditizing Data-Intensive Biocomputing in the Cloud."  This award is under the "Computing in the Cloud" solicitation, a joint venture between NSF and Microsoft  (see  http://www.nsf.gov/news/news_summ.jsp?cntn_id=116336).  In this program, as part of the NSF grant, Microsoft will provide three years of access to its Windows Azure platform for selected researchers.  Microsoft researchers and developers will work with grant recipients as well. 

This project was one of three life science projects chosen in this program.
(see http://www.genomeweb.com/informatics/nsf-gives-three-life-science-projects-12m-grants-test-microsofts-azure-cloud).   This award and another award given to a VT ECE professor Kwa-Sur Tam, were featured as 2 of the 13 successful research teams in the Computing in the Cloud competition, written up in the ACM Tech News on Friday, April 22nd.

Dr. Feng describes the project: "The Microsoft Azure cloud holds the promise of commoditizing high-performance computing by enabling the capability of provisioning large amounts of compute and storage capability via an easy-to-use interface to the cloud.  However, unlike traditional HPC platforms that offer high-speed data-management techniques via parallel file systems and parallel I/O libraries atop high-speed network interconnects, cloud infrastructures like Microsoft Azure do not offer such environments. To address this void while not making any changes to the cloud infrastructure itself, we propose to:

1. create a new generation of efficient data management and analysis software for large-scale, data-intensive scientific applications in the cloud.

2. enhance the robustness of our data management and analysis software for large-scale, data-intensive scientific applications in the cloud.
3. Extend, refine, and optimize our semantic- based data management framework,  to massively accelerate the data movement of large volumes of data."