Faculty Alexander Barg
National Science Foundation
Modern-day data centers store large volumes of information in distributed form, placing parts of the same data file on different servers in the system. Unfortunately, servers fail on a regular basis, either transiently or permanently. When they do, recovering the data stored on them depends on methods of data encoding. Developing these methods is critical to designing large-scale storage systems.
“From Storage Codes to Recoverable Systems” is a $500K National Science Foundation grant. In this project, Alexander Barg will construct methods of data encoding that account for low-cost data recovery based on the nature of connections between the servers such as spatial proximity or the availability of communication links.
The project represents a shift from the broadly studied problems of data reconstruction that discount the varying cost of moving data between servers based on the topology of the system and opens a possibility of engaging new mathematical methods for designing efficient methods of data encoding and reconstruction.
Barg will advance high-density storage systems based on recently discovered applications of computer science and applied mathematics tools to the code design. He will also establish new statistical properties of methods of data encoding as well as the limits on the volume of data that can be stored in the system while maintaining the recovery functionality.