The computational Grid has become an important infrastructure as an execution environment for scientific applications that require large amount of computing resources.  Applications which would otherwise be unmanageable or take a prohibitively longer execution time under previous computing paradigms can now be executed efficiently on the Grid within a reasonable time.

Multi-agent based simulation (MABS) is a methodology used to study and understand the dynamics of real world phenomena in domains involving interaction and/or cooperative problem solving where the participants are characterized by entities having autonomous and social behaviour.  For certain domains the size of the simulation is extremely large, intractable without employing adequate computing resources such as the Grid. 

Although the Grid has come with immense opportunities to resource demanding applications such as MABS, it has also brought with it a number of challenges related to performance.  Performance problems may have their origins either on the side of the computing infrastructure or the application itself, or both. 

This thesis aims at improving the performance of MABS applications by overcoming problems inherent to the behaviour of MABS applications.  It also studies the extent to which the MABS technologies have been exploited in the field of simulation and find ways to adapt existing technologies for the Grid.  It investigates performance monitoring and prediction systems in the Grid environment and their implementation for MABS application with the purpose of identifying application related performance problems and their solutions.

Our research shows that large-scale MABS applications have not been implemented despite the fact that many problem domains that cannot be studied properly with only partial simulation.  We assume that this is due to the lack of appropriate tools such as MABS platforms for the Grid.  Another important finding of this work is the improvement of application performance through the use of MABS specific middleware.

This research investigates approaches to improve the performance of multi-agent based simulation (MABS) applications executed in distributed computing environments.  MABS is a type of micro-level simulation used to study dynamic systems consisting of interacting entities, and in some cases, the number of the simulated entities can be very large.  Most of the existing publicly available MABS tools are single-threaded desktop applications that are not suited for distributed execution.  For this reason, general-purpose multi-agent platforms with multi-threading support are sometimes used for deploying MABS on distributed resources.  However, these platforms do not scale well for large simulations due to huge communication overheads.  In this research, different strategies to deploy large scale MABS in distributed environments are explored, e.g., tuning existing multi-agent platforms, porting single-threaded MABS tools to distributed environment, and implementing a service oriented architecture (SOA) deployment model.

Although the factors affecting the performance of distributed applications are well known, the relative significance of the factors is dependent on the architecture of the application and the behaviour of the execution environment. We developed mathematical performance models to understand the influence of these factors and, to analyze the execution characteristics of MABS.  These performance models are then used to formulate algorithms for resource management and application tuning decisions.

The most important performance improvement solutions achieved in this thesis include: predictive estimation of optimal resource requirements, heuristics for generation of agent reallocation to reduce communication overhead and, an optimistic synchronization algorithm to minimize time management overhead.  Additional application tuning techniques such as agent directory caching and message aggregations for fine-grained simulations are also proposed.  These solutions were experimentally validated in different types of distributed computing environments.

Another contribution of this research is that all improvement measures proposed in this work are implemented on the application level.  It is often the case that the improvement measures should not affect the configuration of the computing and communication resources on which the application runs.  Such application level optimizations are useful for application developers and users who have limited access to remote resources and lack authorization to carry out resource level optimizations.