Storage systems for large and distributed clusters of compute servers are themselves large and distributed. Their complexity and scale makes it hard to manage these systems, and in particular they make it hard to ensure that applications using them get good, predictable performance. At the same time, shared access to the system from multiple applications, users, and competition from internal system activities leads to a need for predictable performance.
The storage quality-of-service project at the UCSC Storage Systems Research Center investigates mechanisms for improving storage system performance in large distributed storage systems through mechanisms that integrate the performance aspects of the path that I/O operations take through the system, from the application interface on the compute server, through the network, to the storage servers. We focus on five parts of the I/O path in a distributed storage system: I/O scheduling at the storage server, storage server cache management, client-to-server network flow control, client-to-server connection management, and client cache management.
Publications include:
- Tim Kaldewey, Theodore M. Wong, Richard Golding, Anna Povzner, Scott Brandt, and Carlos Maltzahn. Virtualizing disk performance. In Proceedings of the 14th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2008), April 2008 (Best student paper)
- Anna Povzner, Tim Kaldewey, Scott Brandt, Richard Golding, Theodore M. Wong, and Carlos Maltzahn. Efficient guaranteed disk request scheduling with Fahrrad. In Proceedings of the ACM SIGOPS/EuroSys European Conference on Computer Systems 2008 (EuroSys 2008), April 2008
- David O. Bigelow, Suresh Iyer, Tim Kaldewey, Roberto C. Pineiro, Anna Povzner, Scott A. Brandt, Richard A. Golding, Theodore M. Wong, and Carlos Maltzahn. End-to-end performance management for scalable distributed storage. In Proceedings of the Petascale Data Storage Workshop, November 2007
- Theodore M. Wong, Richard A. Golding, Caixue Lin, and Ralph A. Becker-Szendy. Zygaria: Storage performance as a managed resource. In Proceedings of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2006), April 2006