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

R. Xavier, Hendrik Moens, B. Volckaert, F. Turck
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引用次数: 5

Abstract

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.
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云托管弹性媒体服务的自适应虚拟机分配算法
云计算越来越多地用于以前由传统基础设施(包括专用硬件设置)支持的不同服务。其中一个支持云的服务是实时音频/视频协作。现有的基于云计算的协作系统通常是在尽力而为的基础上运行的,并且几乎没有提供交付和质量保证。因此,高质量的以业务为中心的解决方案使用专用的、通常是手动配置的硬件来提供。在本文中,我们提出了在严格的质量要求下提供上述弹性的资源分配算法。使用CloudSim模拟器的扩展版本,利用准备处理季节性和使用预测的现实协作会议模式,对这些算法进行评估。我们的结果表明,与之前设计的更朴素的方法相比,该算法将成本提高了98.38%,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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