Data Grid is a Grid environment for ubiquitous access and analysis of
large-scale data. Because Data Grid is in the early stages of
development, the performance of its petabyte-scale models in a
realistic data processing setting has not been well investigated. 
By enhancing our Bricks Grid simulator to accomodated Data Grid
scenarios, we investigate and compare the performance of different
Data Grid models. These are categorized mainly as either central or
tier models; they employ various scheduling and replication strategies
under realistic assumptions of job processing for CERN LHC experiments
on the Grid Datafarm system. 
Our results show that the central model is efficient but that the tier 
model, with its greater resources and its speculative class of
background replication policies, are quite effective and achieve higher
performance, while each tier is smaller than the central model.