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In this paper, we propose a transformation scheme used to analyze online gaming traffic properties and develop a traffic model. We analyze the packet size and the inter departure time distributions of a popular first‐person shooter game (Left 4 Dead) and a massively multiplayer online role‐playing game (World of Warcraft) in order to compare them to the existing scheme. Recent online gaming traffic is erratically distributed, so it is very difficult to analyze. Therefore, our research focuses on a transformation scheme to obtain new smooth patterns from a messy dataset. It extracts relatively heavy‐weighted density data and then transforms them into a corresponding dataset domain to obtain a simplified graph. We compare the analytical model histogram, the chi‐square statistic, and the quantile‐quantile plot of the proposed scheme to an existing scheme. The results show that the proposed scheme demonstrates a good fit in all parts. The chi‐square statistic of our scheme for the Left 4 Dead packet size distribution is less than one ninth of the existing one when dealing with erratic traffic.

Abstract. In this paper, we proposed an online gaming traffic generator reflecting user behavior patterns. We analyzed the packet size and inter departure time distributions of a popular FPS game (Left4Dead) and MMORPG (World of Warcraft) for regenerating gaming traffic. The proposed traffic generator generates an inter departure time and gaming packet based on analytical model of the gamer behaviors, then transmits the packet according to the inter departure time. Packet generation results show that generated packets of World of Warcraft is much different with analytical model, unlike Left4Dead. It is caused by Nagle algorithm and Delayed Acknowledgments of TCP. Thus, we disabled the Nagle algorithm in the proposed traffic generator. The generation results show that the revised proposed traffic generator guarantees goodness of fit in the generated traffic distribution.

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