Metadata-Rich File Systems
Student: Sasha Ames
Collaborator: Maya Gokhale (LLNL)
Funding: PDSI, LLNL
This project is a LLNL/UCSC collaboration: the goal is to design a scalable metadata-rich file system with database-like data management services. With such a file system scientist will be able to perform time-critical analysis over continually evolving, very large data sets.
In the first phase we designed and implemented QUASAR, a path-based query language using the POSIX IO data model extended by relational links. We conducted a couple of data mining case studies where we compared the baseline architecture consisting of a database and a file system with our MRFS prototype. The QUASAR interface via its query language provides much easier access to large data sets than POSIX IO. MRFS’ querying performance is significantly better than the baseline system due to QUASAR's hierarchical scoping.
Challenges remain and we are in the process of addressing them: we are working on a scalable physical data model of QUASAR's logical data model, and we are designing a rich-metadata client cache to address small update overheads and metadata coherence.