Improving the performance of distributed multi-agent based simulation

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