  Since the mid 1990s, grid computing systems have emerged as an analogy for
making computing power as pervasive an easily accessible as an electric power
grid. Since then, grid computing systems have been shown to be able to provide
very large amounts of storage and computing power to mainly support the
scientific and engineering research on a wide geographic scale. Understanding
the workload characteristics incoming to such systems is a milestone for the
design and the tuning of effective resource management strategies. This is
accomplished through the workload characterization, where workload
characteristics are analyzed and a possibly realistic model for those is
obtained. In this paper, we study the workload of some real grid systems by
using a data mining approach to build a workload model for job interarrival
time and runtime, and a Bayesian approach to capture user correlations and
usage patterns. The final model is then validated against the workload coming
from a real grid system.
