Adaptive server selection for large scale interactive online games

  • Authors:

  • Kang-Won Lee;Bong-Jun Ko;Seraphin Calo

  • Affiliations:

  • IBM Thomas J. Watson Research Center, 19 Skyline Dr., Hawthorne, NY 10532, USA;Columbia University, New York, NY 10027, USA;IBM Thomas J. Watson Research Center, 19 Skyline Dr., Hawthorne, NY 10532, USA

  • Venue:
  • Computer Networks: The International Journal of Computer and Telecommunications Networking
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Large scale interactive online games aim to support a very large number of game players simultaneously. To support hundreds of thousands of concurrent players, game providers have so far focused on developing highly scalable game server architectures and extensible network infrastructures. Recently, distributed online games are beginning to incorporate more interactive features and action sequences; thus, it becomes increasingly important to provision server resources in an efficient manner to support real-time interaction between the users. In this paper, we present a novel distributed algorithm to select game servers for a group of clients participating in a large scale interactive online game session. The goal of server selection is to minimize the server resource usage while satisfying a real-time delay constraint. We develop a synchronization delay model for interactive games and formulate the server selection problem, and prove that the considered problem is NP-hard. The proposed algorithm, called zoom-in-zoom-out, is adaptive to session dynamics (e.g., clients join) and lets the clients select appropriate servers in a distributed manner such that the server resource is efficiently utilized. Using simulation, we study the performance of the proposed algorithm and show that it is simple, yet effective in achieving its design goal. In particular, we show that the performance of our algorithm is comparable to, or sometimes even better than, that of centralized greedy algorithms, which require global information and extensive computations.