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Themis: Efficient and Adaptive Resource Partitioning for Reducing Response Delay in Cloud Gaming

Cloud gaming has been increasing in popularity recently, but issues relating to maintaining low interaction delay for users to guarantee satisfactory gaming experience is still prevalent. Interaction delays caused by server-side processing are heavily influenced by how the processes partition the resources. However, finding the optimal partitioning policy that minimizes the response delay is complicated by several critical challenges. In this paper, we propose Themis, a system that enables efficient and adaptive online resource partitioning for reducing response delay in cloud gaming. Briefly, Themis employs machine learning technology to build a performance model which is able to capture the complex relationships between resource partition and system performance. With this model, Themis divides the processes into disjoint groups and partitions resources among process groups, which greatly simplifies the resource partition problem while ensuring high partitioning effectiveness. To tackle dynamic workload changes, Themis leverages reinforcement learning to learn how different partitioning actions affect system performance in an online manner, and adaptively choose the best actions for minimizing response delay in real time. We evaluate Themis in a real cloud gaming environment using several real games. The experimental results show that Themis can reduce the response delay by 17% to 36% compared to a system without resource partitioning, and outperforms other resource partitioning policies significantly. To the best of our knowledge, this is the first work to optimize response delay in cloud gaming through resource partitioning.