Early Experiences on the Journey Towards Self-* Storage
Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, September 2006.
Michael Abd-El-Malek, William V. Courtright II, Chuck Cranor, Gregory R. Ganger, James Hendricks, Andrew J. Klosterman, Michael Mesnier, Manish Prasad, Brandon Salmon, Raja R. Sambasivan, Shafeeq Sinnamohideen, John D. Strunk, Eno Thereska, Matthew Wachs, Jay J. Wylie*
Parallel Data Laboratory
Carnegie Mellon University
Pittsburgh, PA 15213
*HP Labs, Palo Alto, CA
Self-* systems are self-organizing, self-configuring, self-healing, self-tuning and, in general, selfmanaging. Ursa Minor is a large-scale storage infrastructure being designed and deployed at Carnegie Mellon University, with the goal of taking steps towards the self-* ideal. This paper discusses our early experiences with one specific aspect of storage management: performance tuning and projection. Ursa Minor uses self-monitoring and rudimentary system modeling to support analysis of how system changes would affect performance, exposing simple What...if query interfaces to administrators and tuning agents. We find that most performance predictions are sufficiently accurate (within 10-20%) and that the associated performance overhead is less than 6%. Such embedded support for What...if queries simplifies tuning automation and reduces the administrator expertise needed to make acquisition decisions.