Modern data centers are becoming the next computing platform for the Internet. These massive farms of computer servers are an indispensable component of the information age, enabling applications like web search, social networks, cloud computing, as well as distributed file and video sharing. The problem of storing and processing massive amounts of information over networks is significant and challenging. Still, most existing distributed storage systems represent information by suboptimal replication schemes combined with ad-hoc networking protocols. This research is focused on understanding, on a fundamental level, how to represent, store, and process information in distributed storage systems.
Specifically, this research involves the design of novel distributed storage codes that use network coding theory to address modern storage challenges. As is well known, general multi-source information theory problems can be notoriously intractable. This research focuses on the framework of storage networks and builds on recent results of the PI to investigate: 1) Dynamics of coded storage systems. How to maintain coded information representations over networks. Prior work has demonstrated the key role of network coding for such problems but the fundamental bounds remain unknown for several cases of interest. 2) Interference Alignment for Network Coding. Interference alignment techniques were introduced to mitigate wireless interference at the physical layer but also have surprising applicability in distributed storage and other network coding problems. This research is developing a novel mathematical framework that interprets interference alignment techniques through optimization theory. This project is strongly coupled with educational developments and outreach through an online wiki bibliography and online tutorial presentations.
