Hopkins Storage Systems Lab

Storage and Database Systems for Science and Engineering

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This Web page is now defunct.  The HSSL will no longer be maintaining an independent Web presence.  Please refer to the specific research projects, institutes and teams that we work with.  These include:

<Material below is from 2014 or earlier>

The Hopkins Storage Systems Lab (HSSL) is dedicated to building storage and database systems that address emerging requirements in scientific, high-performance, and distributed computing. Towards these goals, the lab conducts research into: scientific data management, scalable storage systems, data archival and preservation, and storage security.

New PhD Students: The Hopkins Storage Systems lab is seeking talented students in the areas of storage systems, operating systems, secure systems, and high-performance computing to join the lab as PhD students.  PhD students are typically fully supported--tuition, stipend, and health insurance--by Teaching or Research Assistantships.  Please refer to the Department's Graduate Application Information and apply through the University's Online Application.  Indicate Randal Burns as a Faculty member of interest in your application and either Storage and Database Systems or Operating Systems as your areas of interest.



Going to FAST 2012

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Osama Khan will be presenting our paper "Rethinking Erasure Codes for Cloud File Systems: Minimizing I/O for Recovery and Degraded Reads, Osama Khan, Randal Burns, James Plank, William Pierce, and Cheng Huang" at the USENIX File and Storage Technologies on Thursday February 16th in San Jose, CA.  Check out the abstract or get the full version after the conference.

FAST '12
Last Updated on Tuesday, 31 January 2012 04:18


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Randal Burns was nominated to serve as a member of the Defense Science Study Group's Class of 2012-2013.  This should be fun.  http://dssg.ida.org/
Last Updated on Monday, 30 January 2012 20:51

Open Connectome

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We are happy to be hosting 15TB (raw images) of EM image data representing 1154 slices of a mouse brain at a resolution of 3nm x 3 xnm x 40 nm.  Explore the data at openconnectomeproject.org. Soon we will provide a Web service to help researchers develop algorithms that automatically annotate neural structure and visualize those annotations.  A presentation on this service is also available at http://prezi.com/yj6psr8b9-35/the-open-connectome-project/.  This data set was featured on the cover of Nature on March 10, 2011.


Last Updated on Saturday, 19 March 2011 17:00