Grid computing is an emerging technology which has the
potential to deliver high performance computing by
facilitating access to geographically distributed
computing resources and presenting them to the user
as a single unified resource. It is a form of
distributed or parallel computing which utilises
agent technologies to automate activities and
therefore hide complexities from users. An agent is
viewed as a software entity that encapsulates a
well defined functionality such as providing a
particular service. Simulation environments which
are able to accurately model Grid systems are
crucial as they enable the operation of
computational Grids to be examined in a repeatable
manner. This would otherwise not be possible as due
to the nature of Grid systems resources are often
located across multiple administrative domain
boundaries. Consequently it is impossible to gain
access to these resources in order to recreate the
exact system conditions which occurred in previous
experiments. Simulators also play an important role
in designing Grid systems and optimising existing
systems. They allow the operation of a proposed
design to be tested and improved before the actual
system is built and enable the performance and
configuration of existing systems to be examined
and improved. Several metaphors have been proposed
as a basis for Grid systems. This work is based on
the computational economy paradigm and views Grids
as economies of resources, one of the aims of this
project is to examine how employing different
economic models affects the performance of the
overall Grid system. These economies consist of two
basic types of participant, resource providers and
resource consumers. Resource providers allow their
resources to be accessed and are motivated by
financial incentives, they require the highest
possible return on their investment and therefore
require their resources to be utilised as much as
possible. Resource consumers require their
computational jobs to be completed to satisfy their
particular quality of service requirements, for
example to satisfy budget or completion deadline
constraints. A simulation environment would
therefore enable the configurations which produce
the highest overall throughput and achieve the best
load balancing to be determined. It would also be
possible to examine precisely where particular jobs
are executed, which machines are over or under
utilised and the possible effects if parts of the
network were taken offline or the size of the Grid
was increased. This Empirical Modelling (EM)
project is therefore related closely to the field
of Concurrent systems modelling. It is hoped that
employing an EM approach will enable dependencies
present within Grid systems to be more accurately
modelled and so produce more realistic
representations than has previously been possible
using more traditional simulation techniques.