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Investigating Energy Consumption and Performance Trade-Off for Interactive Cloud Application

机译:调查交互式云应用程序的能耗和性能折衷

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With the ever growing demand and popularity of cloud based services, data centers have to urgently face energy consumption issue. Similar to other large consumers of power, data centers find themselves increasingly pressured to reduce their carbon footprint. In response, cloud providers have started to set sustainability goals to reduce carbon emissions by using renewable sources to their services. Traditionally, batch processing cloud applications are deadline oriented, hence they can be easily adapted with the different green energy profile. Whereas, interactive cloud applications are imposed with several performance criteria. This paper, the first of its kind, investigates a thorough analysis of energy consumption and performance trade-off by allowing smart usage of green energy for interactive cloud application. Moreover, we propose an auto-scaler, named SaaScaler, that implements several control loop based application controllers to satisfy different performance (i.e., response time, availability, and user experience) and resource aware metrics (i.e., quality of energy). Based on extensive experiments with RUBiS benchmark and real workload traces using single compute node in Openstack/Grid'5000, results suggest that 13 percent brown energy consumption can be reduced without deprovisioning any physical or virtual resources at IaaS layer while 29 percent more users can access the application by dynamically adjusting capacity requirements. Furthermore, our investigation verifies that the energy consumption deviates as little as .07 percent when our approach is scaled using several physical nodes.
机译:随着基于云的服务的需求不断增长和普及,数据中心不得不迫切面临能耗问题。与其他大型电力消费者一样,数据中心发现自己承受着越来越大的压力来减少其碳足迹。作为响应,云提供商已开始设定可持续性目标,以通过在服务中使用可再生资源来减少碳排放。传统上,批处理云应用程序面向截止日期,因此可以轻松地使用不同的绿色能源配置文件进行调整。而交互式云应用程序具有多个性能标准。本文是同类文章中的第一篇,它通过允许智能地使用绿色能源用于交互式云应用程序,研究了对能耗和性能折衷的全面分析。此外,我们提出了一种名为SaaScaler的自动缩放器,该缩放器实现了几个基于控制循环的应用程序控制器,以满足不同的性能(即响应时间,可用性和用户体验)和资源感知指标(即能源质量)。基于RUBiS基准测试的广泛实验和使用Openstack / Grid'5000中的单个计算节点的实际工作负载跟踪的结果,结果表明,无需在IaaS层上部署任何物理或虚拟资源,就可以减少13%的棕色能源消耗,而更多的29%的用户可以访问通过动态调整容量需求来应用程序。此外,我们的研究证实,当我们的方法使用多个物理节点进行扩展时,能源消耗偏差仅为0.07%。

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