Behavior and Performance of Interactive Multi-Player Game Servers

  • Authors:

  • Ahmed Abdelkhalek;Angelos Bilas;Andreas Moshovos

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, ON, Canada M5S 3G4 abdel@eecg.toronto.edu;Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, ON, Canada M5S 3G4 bilas@eecg.toronto.edu;Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, ON, Canada M5S 3G4 moshovos@eecg.toronto.edu

  • Venue:
  • Cluster Computing
  • Year:
  • 2003

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Abstract

With the recent explosion in deployment of services to large numbers of customers over the Internet and in global services in general, issues related to the architecture of scalable servers are becoming increasingly important. However, our understanding of these types of applications is currently limited, especially on how well they scale to support large numbers of users. One such, novel, commercial class of applications, are interactive, multi-player game servers. Multi-player games are both an important class of commercial applications (in the entertainment industry) and they can be valuable in understanding the architectural requirements of scalable services. They impose requirements on system performance, scalability, and availability, stressing multiple aspects of the system architecture (e.g., compute cycles and network I/O). Recently there has been a lot of interest on client side issues with respect to games. However, there has been little or no work on the server side. In this paper we use a commercial game server to gain insight in this class of applications and the requirements they impose on modern architectures. We find that: (1) In terms of the benchmarking methodology, interactive game servers are very different from scientific workloads. We propose a methodology that deals with the related issues in benchmarking this class of applications. Our methodology bears many similarities with methodologies used in benchmarking online transaction processing (OLTP) systems. (2) Current, sequential game servers can support at most up to a few tens of users (60–100) on existing processors. (3) The bottleneck in the server is both game-related as well as network-related processing (about 50–50). (4) Network bandwidth requirements are not an important issue for the numbers of players we are interested in. (5) The processor achieves a surprisingly low IPC of 0.416.