Comparing interest management algorithms for massively multiplayer games

  • Authors:

  • Jean-Sébastien Boulanger;Jörg Kienzle;Clark Verbrugge

  • Affiliations:

  • McGill University, Montréal, Canada;McGill University, Montréal, Canada;McGill University, Montréal, Canada

  • Venue:
  • NetGames '06 Proceedings of 5th ACM SIGCOMM workshop on Network and system support for games
  • Year:
  • 2006

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Abstract

Broadcasting all state changes to every player of a massively multiplayer game is not a viable solution. To successfully overcome the challenge of scale, massively multiplayer games have to employ sophisticated interest management techniques that only send relevant state changes to each player. This paper compares the performance of different interest management algorithms based on measurements obtained in a real massively multiplayer game using human and computer-generated player actions. We show that interest management algorithms that take into account obstacles in the world reduce the number of update messages between players by up to a factor of 6, and that some computationally inexpensive tile-based interest management algorithms can approximate ideal visibility-based interest management at very low cost. The experiments also show that measurements obtained with computer-controlled players performing random actions can approximate measurements of games played by real humans, provided that the starting positions of the random players are chosen adequately. As the size of the world and the number of players of massively multiplayer games increases, adaptive interest management techniques such as the ones studied in this paper will become increasingly important.