PROJECT TITLE :

Play Request Dispatching for Efficient Virtual Machine Usage in Cloud Gaming

ABSTRACT:

Cloud gaming is changing into increasingly standard. The basic idea of cloud gaming is to run games on cloud servers and let players interact with games through skinny shoppers. As the player population grows, the cloud gaming service supplier wants to take care of a large range of cloud servers for running the sport instances requested by the players. A primary concern of the cloud gaming service provider is the total running price of the cloud servers. In this paper, we study the matter of a way to dispatch the play requests to the cloud servers in a very cloud gaming system. We show that the dispatching strategy of play requests may heavily have an effect on the total service price of the cloud gaming system. The play request dispatching drawback will be thought of as a variant of the dynamic bin packing downside. But, we tend to show that the classical bin packing algorithms like 1st Work (FF) and Best Work (BF) don't seem to be efficient in terms of resource usage in cloud gaming due to the diurnal workload pattern of on-line games. To deal with this issue, we tend to propose an economical request dispatching algorithm that assigns play requests consistent with the predicted ending times of game sessions. We also assess several categories of prediction algorithms and select a neural-network-based algorithm to predict the ending times of game sessions. We conduct intensive evaluations of the proposed algorithms using real traces from different sorts of on-line games. The experimental results show that the proposed dispatching algorithm with neural-network-based mostly prediction will reduce the resource waste of the cloud servers and thus decrease the whole service price compared to the FF and BF algorithms. The reduction in the resource waste is particularly significant for match-primarily based games like Defense of the Ancient and World of Tank.


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