Cloud gaming has been very popular recently, but providing satisfactory gaming experiences to players at a modest cost is still challenging. Colocating several games onto one server could improve server utilization. However, prior work regarding colocating games either ignores the performance interference between games or uses simple performance model to charaterize it, which may make inefficient game colocation decisions and cause QoS violations. In this article, we address the resource allocation issues for colocating games in cloud gaming. We first propose a novel machine learning-based performance model, which is able to capture the complex relationship among the performance interference, the contention features of colocated games and resource partition. Guided by the performance model, we then propose efficient and effective algorithms for two resource allocation scenarios in cloud gaming. We evaluate the proposed solutions through extensive experiments using a large number of real popular games. The results show that our performance model is able to identify whether a colocated game satisfies QoS requirement within an average error of 5 percent, which significantly outperforms the alternatives. Our resource allocation algorithms are able to increase the resource utilization by up to 60 percent compared to the state-of-the-art solutions.

中文翻译：

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在云游戏中通过 QoS 保证最大限度地减少资源使用

云游戏近来非常火爆，但以适中的成本为玩家提供满意的游戏体验仍然具有挑战性。将多个游戏并置到一台服务器上可以提高服务器利用率。然而，先前关于托管游戏的工作要么忽略游戏之间的性能干扰，要么使用简单的性能模型对其进行表征，这可能会导致游戏托管决策效率低下并导致 QoS 违规。在本文中，我们解决了在云游戏中托管游戏的资源分配问题。我们首先提出了一种新颖的基于机器学习的性能模型，该模型能够捕捉性能干扰、协同定位游戏的竞争特征和资源划分之间的复杂关系。以性能模型为指导，然后，我们为云游戏中的两种资源分配场景提出了高效且有效的算法。我们通过使用大量真实流行游戏的广泛实验来评估所提出的解决方案。结果表明，我们的性能模型能够在 5% 的平均误差内识别并置游戏是否满足 QoS 要求，这显着优于替代方案。与最先进的解决方案相比，我们的资源分配算法能够将资源利用率提高多达 60%。结果表明，我们的性能模型能够在 5% 的平均误差内识别并置游戏是否满足 QoS 要求，这显着优于替代方案。与最先进的解决方案相比，我们的资源分配算法能够将资源利用率提高多达 60%。结果表明，我们的性能模型能够在 5% 的平均误差内识别并置游戏是否满足 QoS 要求，这显着优于替代方案。与最先进的解决方案相比，我们的资源分配算法能够将资源利用率提高多达 60%。