As part of a broader organisational restructure, data networking research at Swinburne University of Technology has moved from the Centre for Advanced Internet Architecture (CAIA) to the Internet For Things (I4T) Research Lab.

Although CAIA no longer exists, this website reflects CAIA's activities and outputs between March 2002 and February 2017, and is being maintained as a service to the broader data networking research community.

GENIUS Program Overview


GENIUS projects cover a range of areas relating to networked, online multiplayer games. These include:
  • Short- and long-time frame traffic patterns
  • Sensitivities of game players to network characteristics (such as packet latency and loss)
  • Network layer mechanisms for cheat-mitigation & Fairness between game players experiencing different network characteristics
  • Hidden network-layer impact of server discovery protocols
  • Synthetic construction of realistic game traffic simulations
  • Passive real-time detection of live game traffic in ISP access networks
  • The use of 3D game engines for interactive visualisation of network activity
Brief descriptions of each area follows below, with links to related papers, results and/or tools.

Short- and long-time frame traffic patterns
The first step in understanding the impact of game traffic is to capture and measure it under controlled conditions.
Of interest when investigating IP QoS issues are short timescale characteristics of IP traffic while games are in progress (such as geographic/topological diversity of participating network endpoints, inter-packet arrival times and packet size distributions). For long term trend planning we're interested in long timescale aggregate usage (e.g. over hours, days, and weeks). We have collected short packet traces for internal use, and made a small collection of traces publicly available under the SONG project. (Publications.)

Realistic game traffic simulation
Our ultimate goal behind the collection of real-world traffic is to construct plausible, synthetic traffic models. Such traffic models should enable predictions to be made about the interactions between the traffic of interactive games and other network applications. We have developed some increasingly sophisticated ideas on modeling multiplayer FPS (first person shooter) traffic.

Sensitivities of game players to network characteristics
Game play involves reacting to events in a virtual world. As the interactivity goes up, the need for rapid and reliable network communication increases. We have done some small scale analyses of the sensitivity of FPS players to latency and packet loss.

Cheat-mitigation & Fairness
Related to the sensitivity of players to network characteristics, we have explored the possibility of introducing artificial network degradation to punish cheaters, and looked at how imbalanced network behaviour influences fairness between players. (Publications.)

Server discovery protocols
Before online games are played, suitable servers must be discovered. We have explored some ideas for optimising the search process required to find game servers with suitably low latency.

Detection of live game traffic
We have explored the use of Machine Learning techniques for game traffic detection / identification. Our interest in ML techniques leveraged work done in the DSTC project (suppored by Cisco Systems), and culminated in the
ANGEL project (suppored by the Smart Internet Technologies CRC). (Publications.)

3D game engines for interactive visualisation of network activity
Under the auspices of the L3DGE project we have explored the possibilities of leveraging multiplayer FPS game engines to simplify the monitoring of abstract, multi-metric systems (such as networks themselves). (Publications.)

Last Updated: Thursday 3-Apr-2008 11:52:08 AEDT | No longer maintained. Pre-2018 was maintained and authorised by Grenville Armitage,