  The data access patterns of applications running in computing grids are
changing due to the recent proliferation of high speed local and wide area
networks. The data-intensive jobs are no longer strictly required to run at the
computing sites, where the respective input data are located. Instead, jobs may
access the data employing arbitrary combinations of data-placement, stage-in
and remote data access. These data access profiles exhibit partially
non-overlapping throughput bottlenecks. This fact can be exploited in order to
minimize the time jobs spend waiting for input data. In this work we present a
novel grid computing simulator, which puts a heavy emphasis on the various data
access profiles. The fundamental assumptions underlying our simulator are
justified by empirical experiments performed in the Worldwide LHC Computing
Grid (WLCG) at CERN. We demonstrate how to calibrate the simulator parameters
in accordance with the true system using posterior inference with
likelihood-free Markov Chain Monte Carlo. Thereafter, we validate the
simulator's output with respect to an authentic production workload from WLCG,
demonstrating its remarkable accuracy.
