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Adaptive virtual machine allocation algorithms for cloud-hosted elastic media services

机译:云托管弹性媒体服务的自适应虚拟机分配算法

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Cloud computing is growing in adoption for different services previously supported by traditional infrastructure, including dedicated hardware setups. One of these cloud-enabled services is real-time Audio/Video collaboration. Existing cloud-based collaboration systems generally function on a best-effort basis, and offer little to no delivery and quality guarantees. High-quality business-focused solutions are therefore offered using dedicated and often manually configured hardware. In this paper, we present resource provisioning algorithms to provide the mentioned elasticity under strict quality requirements. These algorithms are evaluated, using an extended version of the CloudSim simulator, making use of realistic collaborative meeting patterns prepared to deal with seasonality and usage prediction. Our results show that the algorithms improve costs by up to 98.38% when compared with previously designed more naive approaches and with an effectiveness of 99.9% in meeting A/V collaboration setup deadlines.
机译:云计算越来越多地被以前由传统基础架构支持的各种服务(包括专用硬件设置)采用。这些支持云的服务之一是实时音频/视频协作。现有的基于云的协作系统通常是在尽力而为的基础上运行的,几乎没有交付或没有质量保证。因此,可以使用专用且通常手动配置的硬件来提供高质量的以业务为中心的解决方案。在本文中,我们提出了资源供应算法,以在严格的质量要求下提供上述弹性。使用CloudSim模拟器的扩展版本对这些算法进行评估,并利用为应对季节性和使用情况预测而准备的现实协作会议模式。我们的结果表明,与以前设计的更幼稚的方法相比,该算法可将成本提高98.38%,并且在满足A / V协作设置截止日期方面的效率为99.9%。

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