Massively multiplayer online role playing games (MMORPGs) have become extremely popular among network gamers. Despite their success, one of MMORPG's greatest challenges is the increasing use of game bots, that is, autoplaying game clients. The use of game bots is considered unsportsmanlike and is therefore forbidden. To keep games in order, game police, played by actual human players, often patrol game zones and question suspicious players. This practice, however, is labor-intensive and ineffective. To address this problem, we analyze the traffic generated by human players versus game bots and propose general solutions to identify game bots. Taking Ragnarok Online as our subject, we study the traffic generated by human players and game bots. We find that their traffic is distinguishable by 1) the regularity in the release time of client commands, 2) the trend and magnitude of traffic burstiness in multiple time scales, and 3) the sensitivity to different network conditions. Based on these findings, we propose four strategies and two ensemble schemes to identify bots. Finally, we discuss the robustness of the proposed methods against countermeasures of bot developers, and consider a number of possible ways to manage the increasingly serious bot problem.
Online gaming has become increasingly popular in recent years. Currently, the most common business model of online gaming is based on monthly subscription fees that game players pay to obtain credits, which allow them to start or continue a journey in the game's virtual world. Therefore, from the perspective of game operators, predicting how many players will join a game and how long they will stay in the game is important since these two factors dominate their revenue.This paper represents a pilot study of the predictability of online gamers' subscription time. Specifically, we study the gameplay hours of online gamers and investigate whether strong patterns are embedded in their game hours. Our ultimate goal is to provide a prediction model of online gamers, which takes a player's game hours as the input and predicts whether the player will leave in the near future. Our study is based on real-life traces collected from World of Warcraft, a famous MMORPG (Massively Multiplayer Online RolePlaying Game). The traces contain the gameplay histories of 34, 524 players during a two-year period. We believe that our study would be useful for building a prediction model of players' future game hours and unsubscription decisions; i.e., decisions not to renew subscriptions.
While cloud servers provide a tremendous amount of resources for networked video applications, most successful stories of cloud-assisted video applications are presentational video services, such as YouTube and NetFlix. This article surveys the recent advances on delay-sensitive video computations in the cloud, which are crucial to cloud-assisted conversational video services, such as cloud gaming, Virtual Reality (VR), Augmented Reality (AR), and telepresence. Supporting conversational video services with cloud resources is challenging because most cloud servers are far away from the end users while these services incur the following stringent requirements: high bandwidth, short delay, and high heterogeneity. In this article, we cover the literature with a top-down approach: from applications and experience, to architecture and management, and to optimization in and outside of the cloud. We also point out major open challenges, hoping to stimulate more research activities in this emerging and exciting direction.
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