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Energy proportionality in near-threshold computing servers and cloud data centers: Consolidating or Not?

机译:接近阈值的计算服务器和云数据中心中的能源比例:是否合并?

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Cloud Computing aims to efficiently tackle the increasing demand of computing resources, and its popularity has led to a dramatic increase in the number of computing servers and data centers worldwide. However, as effect of post-Dennard scaling, computing servers have become power-limited, and new system-level approaches must be used to improve their energy efficiency. This paper first presents an accurate power modelling characterization for a new server architecture based on the FD-SOI process technology for near-threshold computing (NTC). Then, we explore the existing energy vs. performance trade-offs when virtualized applications with different CPU utilization and memory footprint characteristics are executed. Finally, based on this analysis, we propose a novel dynamic virtual machine (VM) allocation method that exploits the knowledge of VMs characteristics together with our accurate server power model for next-generation NTC-based data centers, while guaranteeing quality of service (QoS) requirements. Our results demonstrate the inefficiency of current workload consolidation techniques for new NTC-based data center designs, and how our proposed method provides up to 45% energy savings when compared to state-of-the-art consolidation-based approaches.
机译:云计算旨在有效应对不断增长的计算资源需求,其普及程度导致全球计算服务器和数据中心的数量急剧增加。但是,由于后登纳尔德(Dennard)缩放的影响,计算服务器已经受到功率限制,必须使用新的系统级方法来提高其能源效率。本文首先针对基于近阈值计算(NTC)的FD-SOI处理技术,提出了一种针对新服务器架构的准确功率建模特性。然后,当执行具有不同CPU利用率和内存占用量特征的虚拟化应用程序时,我们探讨了现有的能耗与性能之间的权衡。最后,在此分析的基础上,我们提出了一种新颖的动态虚拟机(VM)分配方法,该方法利用VM的特性知识以及我们用于下一代基于NTC的数据中心的准确服务器功率模型,同时保证了服务质量(QoS) ) 要求。我们的结果证明了当前的工作负载整合技术对于基于NTC的新数据中心设计的效率低下,并且与基于整合的最新技术相比,我们提出的方法如何节省多达45%的能源。

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