  The next generation of scientific experiments and studies, popularly called
as e-Science, is carried out by large collaborations of researchers distributed
around the world engaged in analysis of huge collections of data generated by
scientific instruments. Grid computing has emerged as an enabler for e-Science
as it permits the creation of virtual organizations that bring together
communities with common objectives. Within a community, data collections are
stored or replicated on distributed resources to enhance storage capability or
efficiency of access. In such an environment, scientists need to have the
ability to carry out their studies by transparently accessing distributed data
and computational resources. In this paper, we propose and develop a Grid
broker that mediates access to distributed resources by (a) discovering
suitable data sources for a given analysis scenario, (b) suitable computational
resources, (c) optimally mapping analysis jobs to resources, (d) deploying and
monitoring job execution on selected resources, (e) accessing data from local
or remote data source during job execution and (f) collating and presenting
results. The broker supports a declarative and dynamic parametric programming
model for creating grid applications. We have used this model in grid-enabling
a high energy physics analysis application (Belle Analysis Software Framework).
The broker has been used in deploying Belle experiment data analysis jobs on a
grid testbed, called Belle Analysis Data Grid, having resources distributed
across Australia interconnected through GrangeNet.
