Relative fitness is a new approach to modeling the performance of storage devices (e.g., disks and RAID arrays). In contrast to a conventional model, which predicts the per- formance of an application’s I/O on a given device, a relative fitness model predicts performance differences between devices. The result is significantly more accurate predictions.

Self-* systems are self-organizing, self-configuring, self-healing, self-tuning and, in general, self- managing. 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.