Interest management for massively multiplayer games

The popularity of massively multiplayer games has increased in recent years and game providers are facing scalability problems to accomodate growing populations of users. Broadcasting all state changes to every player is not a viable solution to maintain a consistent game state in a massively multiplayer game. 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. In this thesis we develop a space partitioning technique based on triangulation that adapts to the world’s obstacles. We introduce obstacle-aware interest management algorithms that use the triangular partitioning to determine the relevance of objects based on the occlusion created by obstacles. We compare the performance of both obstacle-aware and state-of-the-art interest management algorithms based on measurements obtained in a real massively multiplayer game using human and computer-generated player actions. We show that obstacle-aware interest management algorithms can reduce the number of update messages between players and that algorithms based on our triangle-based partitioning can scale to a larger number of objects. 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 traces of the random players are designed 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 thesis will become increasingly important.

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