Dynamically learning efficient server/client network protocols for networked simulations

• Author / Creator

  Orsten, Sterling

• With the rise of services like Steam and Xbox Live, multiplayer support has become essential to the
  success of many commercial video games. Explicit, server-client synchronisation models are bandwidth
  intensive and error prone to implement, while implicit, peer-to-peer synchronisation models
  are brittle, inflexible, and vulnerable to cheating. We present a generalised server-client network
  synchronisation model targeted at complex games, such as real time strategy games, that previously
  have only been feasible via peer-to-peer techniques. We use prediction, learning, and entropy coding
  techniques to learn a bandwidth-efficient incremental game state representation while guaranteeing
  both correctness of synchronised data and robustness in the face of unreliable network behavior. The
  resulting algorithms are efficient enough to synchronise the state of real time strategy games such
  as Blizzard’s Starcraft (which can involve hundreds of in-game characters) using less than three
  kilobytes per second of bandwidth.

• Subjects / Keywords

  • Games
  • Networking
  • Software engineering
  • Compression

• Graduation date

  Spring 2011

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3JG6X

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.