Modelling user behaviour in networked games
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Provisioning on-line games: a traffic analysis of a busy counter-strike server
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Latency and User Behaviour on a Multiplayer Game Server
NGC '01 Proceedings of the Third International COST264 Workshop on Networked Group Communication
Game traffic analysis: an MMORPG perspective
Computer Networks: The International Journal of Computer and Telecommunications Networking
An aircraft cabin wireless system for games and video entertainment
Computers in Entertainment (CIE) - Interactive entertainment
Measurement-based characterization of a collection of on-line games
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
World of warcraft avatar history dataset
MMSys '11 Proceedings of the second annual ACM conference on Multimedia systems
Review: Packet-level traffic analysis of online games from the genre characteristics perspective
Journal of Network and Computer Applications
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DS-RT '12 Proceedings of the 2012 IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications
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One of the most important aspects in determining the global traffic characteristics of on-line games is to model the traffic behavior of the client. While modeling the client ON-OFF times in web traffic has allowed researchers to generate accurate fractal models of aggregate web traffic, similar work has not yet been done for the domain of games. Previous work in client traffic behavior has shown that clients consume a nearly constant amount of resources when they are on. However, the distribution of the length of ON times for clients has not yet been established [1]. In this paper, we study the player session time distribution over a one-week trace of a popular on-line game server. Our results indicate that the session ON times of game clients steeply decays over time, with a knee at about 15--30 minutes. In addition, we show that the session time PDF can be fitted accurately with a Weibull distribution with parameters (β = 0.5, η = 20, and γ = 0), a result that diverges from previous studies of player session times [2].