Dynamic grid-based approach to data distribution management

  • Authors:
  • Azzedine Boukerche;Amber Roy

  • Affiliations:
  • Parallel Simulation and Distributed Systems (PARADISE) Research Laboratory, Department of Computer Science, University of North Texas, Denton, Texas;Parallel Simulation and Distributed Systems (PARADISE) Research Laboratory, Department of Computer Science, University of North Texas, Denton, Texas

  • Venue:
  • Journal of Parallel and Distributed Computing - Parallel and Distributed Discrete Event Simulation--An Emerging Technology
  • Year:
  • 2002

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Abstract

Data distribution management (DDM) is one of the services defined by the DoD High Level Architecture and is necessary to provide efficient, scalable mechanisms for distributing state updates and interaction information in large scale distributed simulations. In this paper, we focus on data distribution management mechanisms (also known as filtering) used for real time training simulations. We propose a new method of DDM, which we refer to as the dynamic grid-based approach. Our scheme is based on a combination of a fixed grid-based method, known for its scalability, and a region-based strategy, which provides greater accuracy than the fixed grid-based method. We describe our DDM algorithm, its implementation, and report on the performance results that we have obtained using the RTI-Kit framework. Our results clearly indicate that our scheme is scalable and that it reduces the message overhead by 40%, and the number of multicast groups used by 98% when compared to the fixed grid-based allocation scheme using 10 nodes, 1000 objects, and 20,000 grid cells.