In recent years, Massively Multiplayer Online Games (MMOGs) are becoming popular, partially due to their sophisticated graphics and broad virtual world, and cloud gaming is demanded more than ever especially when entertaining with light and portable devices. This article considers the problem of server allocation for running MMOG on cloud, aiming to reduce the cost on cloud gaming service and meanwhile enhance the quality of service. The problem is formulated into minimizing an objective function involving the cost of server rental, the cost of data transfer and the network latency during the gaming time. A genetic algorithm is developed to solve the minimization problem for processing simultaneous server allocation for the players who log into the system at the same time while many existing players are playing the same game. Extensive experiments based on the player behavior in “World of Warcraft” are conducted to evaluate the proposed method and compare with the state-of-the-art as well. The experimental results show that the method gives a lower cost and a shorter network latency in most of the time.

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Dead reckoning (DR) is a key technique to increase scalability in Distributed Virtual Environments (DVE). Replacing data transmission with prediction, DR relies on its prediction capability to reduce the bandwidth consumption in the cost of inconsistency among participants. We propose a hot area targeting DR (HATDR) approach to increase the prediction capability by the hot area targeting pattern discovered with a noise-resistant clustering approach. This approach is shown to be robust against hyperparameters. Experiments carried out with a real-life MMOG dataset show that HATDR is comparable to the state-of-the-art DR approaches. CCS CONCEPTS• Information systems → Massively multiplayer online games;• Computing methodologies → Distributed simulation.