Alternative approaches to multicast group management in large-scale distributed interactive simulation systems

  • Authors:
  • Azzedine Boukerche;Caron Dzermajko;Kaiyuan Lu

  • Affiliations:
  • PARADISE Research Laboratory, SITE, University of Ottawa, Canada;PARADISE Research Laboratory, SITE, University of Ottawa, Canada;PARADISE Research Laboratory, SITE, University of Ottawa, Canada

  • Venue:
  • Future Generation Computer Systems - Systems performance analysis and evaluation
  • Year:
  • 2006

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Abstract

A primary concern in implementing and executing large-scale distributed simulations is limiting and controlling the volume of data, regarding simulated entities, exchanged between entities participating in the simulation. Computer scientists in the academic, military and corporate world have spent much time studying and applying various methods of Data Distribution Management (DDM) to learn the strengths and weaknesses of each approach, improve upon existing DDM methods, and discover the most efficient method to use for a particular application. The key to efficient DDM is to successfully limit the data sent to only the data that is needed, and to direct that data to only those entities requiring the data. In this paper, we explain the benefits and goals of DDM, define and describe the various methods of DDM, compare similarities and differences in DDM methods, and discuss several existing DDM implementations. In our discussion of DDM methods, we include Region-Based, Fixed and Dynamic Grid-Based, Hybrid, Agent-Based and Class-Based DDM and compare and contrast these methods and their applications. We also discuss existing High-Level Architecture (HLA)-compliant and non-compliant Run-Time Infrastructures (RTI) and their DDM implementations. Our goal is to promote an understanding of the benefits of DDM and to offer a detailed explanation of the available DDM methods and the RTIs that employ those methods.