Computational Grids, composed of distributed and often heterogeneous
computing resources, have become the platform-of-choice for many
performance-challenged applications. Proof-of-concept implementations
have demonstrated that both Grids and clustered environments have the
potential to provide great performance benefits to distributed
resource-intensive applications. However, at the present time,
careful staging, scheduling, and/or reservation of resources is
essential in order for applications to achieve performance in Grid
environments. If Computational Grids and shared computational
clusters are to achieve their full potential, it must be possible for
users to achieve application performance at any given time, and when
other users are present in the system.
In this paper, we describe the initial development of an AppLeS (Application-Level Scheduler) for the resource
selection portion of the Synthetic Aperture Radar Atlas (SARA)
application, developed at JPL. We demonstrate the effectiveness of
application scheduling for distributed data applications such as SARA
by providing a performance-efficient strategy for retrieving SARA data
files in everyday, multiple-user Grid environments.