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Transformation Approach to Model Online Gaming Traffic
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 g...
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Published in: | ETRI journal 2011, 33(2), , pp.219-229 |
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container_title | ETRI journal |
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creator | Shin, KwangSik Kim, Jinhyuk Sohn, Kangmin Park, Chang Joon Choi, SangBang |
description | 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. |
doi_str_mv | 10.4218/etrij.11.1510.0087 |
format | article |
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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. 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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. 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subjects | analytical model curve fitting Gaming traffic traffic analysis transformation approach 전자/정보통신공학 |
title | Transformation Approach to Model Online Gaming Traffic |
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