Advances in cloud computing and GPU virtualization are allowing the game industry to move into a cloud gaming era. In this paper, we consider multiplayer cloud gaming (MCG), which is the natural integration of multiplayer online gaming and cloud gaming paradigms. With MCG, a game server and a set of rendering servers for the players need to be located and launched in the clouds for each game session. We formulate an MCG server allocation problem with the objective of minimizing the total server rental and bandwidth cost charged by the cloud to support an MCG session. The MCG server allocation problem is hard to solve optimally. We propose several efficient heuristics to address the problem and carry out theoretical analysis for the proposed hill-climbing algorithm. We conduct extensive experiments using real Internet latency and cloud pricing datasets to evaluate the effectiveness of our proposed algorithms as well as several alternatives. Experimental results show that our best algorithm can achieve near-optimal cost under real-time latency constraints.

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Distributed virtual environments (DVEs) are attracting a lot of attention in recent years, due to the increasing popularity of online gaming and social networks. As the number of concurrent users of a DVE increases, a critical problem is on how the workload among multiple servers can be balanced in order to maintain real-time performance. Although a number of load balancing methods have been proposed, they either try to produce high quality load balancing results and become too slow or emphasize on efficiency and the load balancing results become less effective. In this article, we propose a new approach to address this problem based on heat diffusion. Our work has two main contributions. First, we propose a local and a global load balancing methods for DVEs based on heat diffusion. Second, we investigate two performance factors of the proposed methods, the convergence threshold and the load balancing interval. We have conducted a number of experiments to extensively evaluate the performance of the proposed methods. Our experimental results show that the proposed methods outperform existing methods in that our methods are effective in reducing server overloading while at the same time being efficient.

Distributed virtual environments (DVEs) are becoming very popular in recent years, due to their application in online gaming and social networking.One of the main research problems in DVEs is on how to balance the workload when a lot of concurrent users are accessing it. There are a number of load balancing methods proposed to address this problem. However, they either spend too much time on optimizing the partitioning process and become too slow or emphasize on efficiency and the repartitioning process becomes too ineffective. In this paper, we propose a new dynamic load balancing approach for DVEs based on the heat diffusion approach which has been studied in other areas and proved to be very effective and efficient for dynamic load balancing. We have two main contributions. First, we propose an efficient cell selection scheme to identify and select appropriate cells for load migration. Second, we propose two heat diffusion based load balancing algorithms, local and global diffusion. Our results show that the new algorithms are both efficient and effective compared with some existing methods, and the global diffusion method performs the best.

Cloud gaming has gained significant popularity recently due to many important benefits such as removal of device constraints, instant-on, and cross-platform. The properties of intensive resource demands and dynamic workloads make cloud gaming appropriate to be supported by an elastic cloud platform. Facing a large user population, a fundamental problem is how to provide satisfactory cloud gaming service at modest cost. We observe that the software storage cost could be substantial compared to the server running cost in cloud gaming using elastic cloud resources. Therefore, in this article, we address the server provisioning problem for cloud gaming to optimize both the server running cost and the software storage cost. We find that the distribution of game software among servers and the selection of server types both trigger tradeoffs between the software storage cost and the server running cost in cloud gaming. We formulate the problem with a stochastic model and employ queueing theory to conduct a solid theoretical analysis of the system behaviors under different request dispatching policies. We then propose several classes of algorithms to approximate the optimal solution. The proposed algorithms are evaluated by extensive experiments using real-world parameters. The results show that the proposed Ordered and Genetic algorithms are computationally efficient, nearly cost-optimal, and highly robust to dynamic changes.