  Configuring a storage system to better serve an application is a challenging
task complicated by a multidimensional, discrete configuration space and the
high cost of space exploration (e.g., by running the application with different
storage configurations). To enable selecting the best configuration in a
reasonable time, we design an end-to-end performance prediction mechanism that
estimates the turn-around time of an application using storage system under a
given configuration. This approach focuses on a generic object-based storage
system design, supports exploring the impact of optimizations targeting
workflow applications (e.g., various data placement schemes) in addition to
other, more traditional, configuration knobs (e.g., stripe size or replication
level), and models the system operation at data-chunk and control message
level.
  This paper presents our experience to date with designing and using this
prediction mechanism. We evaluate this mechanism using micro- as well as
synthetic benchmarks mimicking real workflow applications, and a real
application.. A preliminary evaluation shows that we are on a good track to
meet our objectives: it can scale to model a workflow application run on an
entire cluster while offering an over 200x speedup factor (normalized by
resource) compared to running the actual application, and can achieve, in the
limited number of scenarios we study, a prediction accuracy that enables
identifying the best storage system configuration.
