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Addressing Cheating and Workload Characterization in Online Games Portland State University PDXScholar Dissertations and Theses Dissertations and Theses 2006 Addressing Cheating and Workload Characterization in Online Games Christopher Chambers Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/open_access_etds Part of the Computer Engineering Commons, and the Computer Sciences Commons Let us know how access to this document benefits ou.y Recommended Citation Chambers, Christopher, "Addressing Cheating and Workload Characterization in Online Games" (2006). Dissertations and Theses. Paper 2669. https://doi.org/10.15760/etd.2665 This Dissertation is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. DISSERTATION APPROVAL The abstract and dissertation of Chris Chambers for the Doctor of Philosophy in Computer Science were presented November 6, 2006, and accepted by the disser- tation committee and the doctoral program. COMMITTEE APPROVALS: Wu-chang Feng, Chair Wu-chi Feng Bryant York Nirupama Bulusu Steve Bleiler Representative of the Office of Graduate Studies DOCTORAL PROGRAM APPROVAL: Cynthia Brown, Director Computer Science Ph.D. Program ABSTRACT An abstract of the dissertation of Chris Chambers for the Doctor of Philosophy in Computer Science presented November 6, 2006. Title: Addressing Cheating and Workload Characterization in On-Line Games The Internet has enabled the popular pastime of playing video games to grow rapidly by connecting game players in disparate locations. However, with popular- ity have come the two challenges of hosting a large number of users and detecting cheating among users. For reasons of control, security, and ease of development, the most popular system for hosting on-line games is the client server architec- ture. This is also the most expensive and least scalable architecture for the game publisher, which drives hosting costs upwards with the success of the game. In addition to the expense of hosting, as a particular game grows more competitive and popular, the incentive to cheat for that game grows as well. All popular on- line games suffer from cheats in one form or another, and this cheating adversely affects game popularity and growth. In this dissertation we follow a hypothetical game company (GameCorp) as it surmounts challenges involved in running an on-line game. We develop a charac- terization of gamer habits and game workloads from data sampled over a period of years, and show the benefits and drawbacks of multiplexing online applications together in a single large server farm. We develop and evaluate a geographic redi- rection service for the public server architecture to match clients with servers. We show how the public server game architecture can be used to scalably host large persistent games such as massively multiplayer (MMO) games that previously used the client server architecture. Finally we develop a taxonomy for client cheating in on-line games to focus research efforts, and specifically treat one of the categories in detail: information exposure in peer-to-peer games. The thesis of this dissertation is: a methodology for accurate usage modeling of server resources can improve workload management; public-server resources can be leveraged in new ways to serve multiplayer on-line games; and that informa- tion exposure in peer-to-peer on-line games is preventable or detectable with the adoption of cryptographic protocols. 2 ADDRESSING CHEATING AND WORKLOAD CHARACTERIZATION IN ON-LINE GAMES by CHRIS CHAMBERS A dissertation submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in COMPUTER SCIENCE Portland State University 2006 Acknowledgments I would like to thank Prof. Wu-chang Feng and Prof. Wu-chi Feng for their invaluable guidance and support at every stage of my progress. Surely the level of advising I was given was above and beyond what was required to nurture a graduate student. I am also very grateful for the insightful comments and feedback given to me by Prof. York, Prof. Bulusu and Prof. Bleiler in the creation of this document and during my academic career. Many thanks to my fellow office-mates and cohorts Ed Kaiser, Jim Snow, Jie Huang and the rest of the SYN department for the collaborative efforts achieved during lunch conversations, on-line game exploration and on the foosball table. Finally my thanks go especially to my wife Evelyn, without whose unlimited sup- port and patience I wouldn’t have lasted a year. i Contents Acknowledgements i List of Tables vi List of Figures vii 1 Introduction 1 1.1 Research Challenges . 2 1.2 History . 3 1.3 Introducing GameCorp . 5 1.4 Thesis Overview . 6 2 Characterizing Game Workloads 9 2.1 Introduction . 9 2.2 Methodology . 13 2.3 Gamers As Individuals . 16 2.3.1 Gamers Are Impatient When Connecting . 16 ii 2.3.2 Gamers Have Short Attention Spans . 19 2.3.3 Gamers Are Not Loyal . 21 2.3.4 Gamers Reveal When They Lose Interest . 23 2.4 Game Populations . 25 2.4.1 Game Popularity Follows a Power-law . 26 2.4.2 Game Workloads Have Varying Degrees of Predictability . 27 2.5 Impact on Infrastructure . 33 2.5.1 Game Workloads are Synchronized . 33 2.5.2 Games and Interactive Application Workloads are Synchro- nized . 35 2.5.3 Games Exhibit Strong Diurnal Geographic Patterns . 38 2.6 Game Updates Significantly Impact Resource Usage . 41 2.7 Conclusions . 45 3 Public Server Games 49 3.1 Introduction . 49 3.2 Public-server Games and Geographic Redirection . 54 3.2.1 Server Selection and Overflow Connections for FPS Games . 54 3.2.2 Methodology . 57 3.2.3 Evaluation . 61 3.2.4 Conclusion . 69 iii 3.3 Public Server Games and Persistent Content . 70 3.3.1 Introduction . 70 3.3.2 Related work . 72 3.3.3 User Resources . 73 3.3.4 Design . 76 3.3.5 Discussion . 84 3.3.6 Conclusion . 87 4 Cheating in On-line Games 89 4.1 Introduction . 89 4.2 A Survey of Cheats . 91 4.2.1 Information Exposure . 95 4.2.2 Game Abstraction . 96 4.2.3 Protocol Cheats . 97 4.2.4 Out-of-path Cheats . 98 4.2.5 Discussion . 99 4.2.6 Dealing with Cheats . 102 4.3 Information Exposure in Peer-to-Peer Games . 104 4.3.1 Background on RTS games . 104 4.3.2 Related Work and Solutions . 106 4.3.3 Protected RTS . 108 iv 4.3.4 Evaluation . 114 4.3.5 Trace-driven Evaluation . 124 4.3.6 Conclusion . 132 5 Conclusion 135 References 142 v List of Tables 2.1 Data sets . 13 2.2 Mean player populations for week of May 23, 2004 . 34 2.3 Web site logs for week of August 13, 2001 . 36 2.4 Connection data for cs.mshmro.com for week of May 23, 2004 . 40 3.1 Redirection experiments . 61 3.2 Average latency and distance reduction for redirected players for experiment e1.............................. 65 3.3 Game architectures . 71 4.1 Examples of cheats in each category for some genres of games . 101 4.2 Data on experiments performed to quantify uncertainty and infor- mation loss . 117 4.3 User traces of Warcraft 3 games . 124 vi List of Figures 2.1 Player impatience based on acceptable refusal ratio . 17 2.2 Session time results for cs.mshmro.com trace . 18 2.3 Player failure rates for individual session times for cs.mshmro.com trace . 22 2.4 CDF of sessions per player on the server . 23 2.5 Player behavior throughout their playing careers . 24 2.6 Game popularity distribution averaged over nine months (log scale) 25 2.7 Player load for three popular games over a 4-week period . 26 2.8 FFT of the player load from four games over one year. 28 2.9 Instantaneous week-to-week PDF of percent load changes for the top 5 most popular games of 2004 . 29 2.10 Mean week-to-week PDF of percent load changes for the top 5 most popular games of 2004 . 30 2.11 Max week-to-week PDF of percent load changes for the top 5 most popular games of 2004 . 31 vii 2.12 Population trends for Half-life and other games after daily and weekly cycles are removed . 32 2.13 Aggregate normalized load across four popular games for week of May 23, 2004 . 35 2.14 Aggregate normalized load between Half-Life and North American cereal manufacturer website . 37 2.15 Aggregate normalized load between Half-Life and North American credit card company . 38 2.16 Aggregate normalized load between Half-Life and International bev- erage manufacturer . 39 2.17 Aggregate normalized load per-continent for cs.mshmro.com . 41 2.18 Normalized load for cs.mshmro.com and the international beverage company website . 42 2.19 Half-Life player population versus Steam CDN usage . 44 2.20 Steam bandwidth during a patch release . 45 2.21 Excess bandwidth consumed by users downloading patches via Steam 46 2.22 Cumulative distribution function of patch data. 47 3.1 MMO subscriber growth over time by game . 51 3.2 Overflow connections on cs.mshmro.com 6/17/03-6/19/03 . 56 3.3 Overview of redirection service architecture . 57 viii 3.4 Redirected connections on cs.mshmro.com 6/16/03-6/17/03 . 62 3.5 Distribution of distance savings in kilometers . 64 3.6 Bad redirects for experiments e1 and e2................ 65 3.7 Plot of distance versus latency for both experiments . 67 3.8 CDF of public server utilization for Half-life 2.
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