Understanding the performance of thin-client gaming

Abstract—The thin-client model is considered a perfect fit for online gaming. As modern games normally require tremendous computing and rendering power at the game client, deploying games with such models can transfer the burden of hardware upgrades from players to game operators. As a result, there are a variety of solutions proposed for thin-client gaming today. However, little is known about the performance of such thin-client systems in different scenarios, and there is no systematic means yet to conduct such analysis. In this paper, we propose a methodology for quantifying the performance of thin-clients on gaming, even for thin-clients which are close-sourced. Taking a classic game, Ms. Pac-Man, and three popular thin-clients, LogMeIn, TeamViewer, and UltraVNC, as examples, we perform a demonstration study and determine that 1) display frame rate and frame distortion are both critical to gaming; and 2) different thin-client implementations may have very different levels of robustness against network impairments. Generally, LogMeIn performs best when network conditions are reasonably good, while TeamViewer and UltraVNC are the better choices under certain network conditions. I

What Can the Temporal Social Behavior Tell Us? An Estimation of Vertex-Betweenness Using Dynamic Social Information

Abstract—The vertex-betweenness centrality index is an es-sential measurement for analyzing social networks, but the computation time is excessive. At present, the fastest algorithm, proposed by Brandes in 2001, requires O(|V ||E|) time, which is computationally intractable for real-world social networks that usually contain millions of nodes and edges. In this paper, we propose a fast and accurate algorithm for estimating vertex-betweenness centrality values for social networks. It only requires O(b2|V |) time, where b is the average degree in the network. Significantly, we demonstrate that the local dynamic information about the vertices is highly relevant to the global betweenness values. The experiment results show that the vertex-betweenness values estimated by the proposed model are close to the real values and their rank is fairly accurate. Furthermore, using data from online role-playing games, we present a new type of dynamic social network constructed from in-game chatting activity. Besides using such online game networks to evaluate our betweenness estimation model, we report several interesting findings derived from conducting static and dynamic social network analysis on game networks. Index Terms—Betweenness, MMORPG, Text-Conversation I

Measuring the latency of cloud gaming systems

Cloud gaming, i.e., real-time game playing via thin clients, relieves players from the need to constantly upgrade their computers and deal with compatibility issues when playing games. As a result, cloud gaming is generating a great deal of interest among entrepreneurs and the public. However, given the large design space, it is not yet known which plat-forms deliver the best quality of service and which design elements constitute a good cloud gaming system. This study is motivated by the question: How good is the real-timeliness of current cloud gaming systems? To ad-dress the question, we analyze the response latency of two cloud gaming platforms, namely, OnLive and StreamMy-Game. Our results show that the streaming latency of On-Live is reasonable for real-time cloud gaming, while that of StreamMyGame is almost twice the former when the StreamMyGame server is provisioned using an Intel Core i7-920 PC. We believe that our measurement approach can be generally applied to PC-based cloud gaming platforms, and that it will further the understanding of such systems and lead to improvements