CAPES: Unsupervised Storage Performance Tuning Using Neural Network-Based Deep Reinforcement Learningwas nominated for a Best Student Paper award at SC17.
Shingled disks have the promise of increasing disk storage density with minimal change to disk recording hardware. However, shingled disks cannot overwrite data in-place, since tracks are written like overlapping shingles. We are exploring data layouts and management techniques that accommodate shingled disk's limitations on write while providing high performance for read. Our research has developed techniques resulting in shingled disks that can, in some cases, exceed the performance of conventional hard disks while preserving the increased capacity.
A partnership between academia and industry exploring and developing new technologies and techniques to improve the manageability, scalability, security, reliability, longevity, and performance of storage systems.
CRSS facilitates collaboration in research and education, and provides pathways to simplify direct transfer of university developed ideas, research results, and technology to its industrial sponsors, helping them to improve their competitive posture in the global marketplace.
CRSS conducts research in a wide range of storage-related fields and applied security, including archival storage, scalable distributed indexing and non-hierarchical file systems, large-scale distributed storage systems, file systems for next-generation storage devices, and data deduplication.
CRSS is looking for talented students who want to study for an M.S. or Ph.D. at UC Santa Cruz! Grad students at CRSS work closely with faculty and other students, as well as with local industry. Our graduates typically have multiple job offers, whether from industry, government, or academia.