I manage the Systems group in Cloud and Information Services Lab at Microsoft. My group focuses on systems infrastructure and machine learning in the “Big data” space. Efforts range from rapid prototyping, to build production quality systems, releasing code to open source, and publishing papers in top Systems conferences. We work closely with Microsoft’s Big Data teams.
As a lab, we work on a wide-range of projects related to analytics at datacenter scale. Our projects span areas such as, cluster resource management, tiered storage, service analytics, query optimization, and stream processing. (Please see the “Projects” tab for details about our projects).
On a personal side, I am broadly interested in building storage and compute infrastructure for datacenter settings. I enjoy building and deploying systems in practice as well as releasing them as open source. In building these systems, my work leverages upon technology trends in datacenter computing.
Some of the previous systems I have built and released as open source projects are:
Kosmos distributed filesystem: I have designed/implemented/deployed (KFS) to manage PB’s of storage.
Sailfish: I have also designed/implemented Sailfish, a compute infrastructure which improves handling of intermediate data (i.e., “shuffle” phase in a Map-Reduce computation). Our results show that Sailfish can improve job completion times at scale by 20% to 5x.
I also collaborate extensively with colleagues in MSR-Redmond.