Performance comparison of data distribution management strategies in large-scale distributed simulation

Data distribution management (DDM) is a High Level Architecture/Run-time Infrastructure (HLA/RTI) service that manages the distribution of state updates and interaction information in large-scale distributed simulations. The key to efficient DDM is to limits and controls the volume of data exchanged during the simulation to relay data to only those hosts requiring the data. This thesis focuses upon different DDM implementations and strategies. This thesis includes analysis of three DDM methods including, the fixed grid-based, dynamic grid-based and region-based methods. Also included is the use of multi-resolution modeling with various DDM strategies and analysis of the performance effects of aggregation/disaggregation with these strategies. Running numerous federation executions, I simulate four different scenarios on a cluster of workstations with a mini-RTI Kit framework and propose a set of benchmarks for a comparison of the DDM schemes. The goals of this work are to determine the most efficient model for applying each DDM scheme, discover the limitations of the scalability of the various DDM methods, evaluate the effects of aggregation/disaggregation on performance and resource usage and present accepted benchmarks for use in future research.