QoE-aware virtual machine placement for cloud games

Hua-Jun Hong, De-Yu Chen, Chun-Ying Huang, Kuan-Ta Chen, Cheng-Hsin Hsu
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引用次数: 28

Abstract

We study an optimization problem to maximize the cloud gaming provider's total profit while achieving just-good-enough Quality-of-Experience (QoE). The optimization problem has exponential running time, and we develop an efficient heuristic algorithm. We also present an alternative formulation and algorithms for closed cloud gaming services, in which the profit is not a concern and overall gaming QoE needs to be maximized. We conduct extensive trace-driven simulations, which show that the proposed heuristic algorithms: (i) achieve close-to-optimal solutions, (ii) always achive 80+% QoE level, and (iii) outperform the state-of-the-art placement heuristic by up to 3.5 times in profits.
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云游戏的qos感知虚拟机布局
我们研究了一个优化问题,以最大化云游戏提供商的总利润,同时达到足够好的体验质量(QoE)。优化问题具有指数级的运行时间,并提出了一种高效的启发式算法。我们还提出了一种封闭云游戏服务的替代公式和算法,其中利润不是问题,整体游戏QoE需要最大化。我们进行了广泛的跟踪驱动模拟,结果表明,所提出的启发式算法:(i)实现接近最优的解决方案,(ii)始终达到80%以上的QoE水平,以及(iii)在利润方面比最先进的布局启发式算法高出3.5倍。
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