Cloud gaming is becoming increasingly popular. The basic idea of cloud gaming is to run games on cloud servers and let players interact with games through thin clients. As the player population grows, the cloud gaming service provider needs to maintain a large number of cloud servers for running the game instances requested by the players. A primary concern of the cloud gaming service provider is the total running cost of the cloud servers. In this paper, we study the problem of how to dispatch the play requests to the cloud servers in a cloud gaming system. We show that the dispatching strategy of play requests may heavily affect the total service cost of the cloud gaming system. The play request dispatching problem can be considered as a variant of the dynamic bin packing problem. However, we show that the classical bin packing algorithms such as First Fit and Best Fit are not efficient in terms of resource usage in cloud gaming due to the diurnal workload pattern of online games.

To address this issue, we propose an efficient request dispatching algorithm that assigns play requests according to the predicted ending times of game sessions. We also assess several classes of prediction algorithms and select a neural-network based algorithm to predict the ending times of game sessions. We conduct extensive evaluations of the proposed algorithms using real traces from different types of online games. The experimental results show that the proposed dispatching algorithm with neural-network based prediction can reduce the resource waste of the cloud servers and thus decrease the total service cost compared to the First Fit and Best Fit algorithms. The reduction in the resource waste is particularly significant for match-based games such as DotA and World of Tank.