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iBrownout: An Integrated Approach for Managing Energy and Brownout in Container-Based Clouds

机译:iBrownout:在基于容器的云中管理能源和电力不足的集成方法

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Energy consumption of Cloud data centers has been a major concern of many researchers, and one of the reasons for huge energy consumption of Clouds lies in the inefficient utilization of computing resources. Besides energy consumption, another challenge of data centers is the unexpected loads, which leads to the overloads and performance degradation. Compared with VM consolidation and Dynamic Voltage Frequency Scaling that cannot function well when the whole data center is overloaded, brownout has shown to be a promising technique to handle both overloads and energy consumption through dynamically deactivating application optional components, which are also identified as containers/microservices. In this work, we propose an integrated approach to manage energy consumption and brownout in container-based cloud data centers. We also evaluate our proposed scheduling policies with real traces in a prototype system. The results show that our approach reduces about 40, 20, and 10 percent energy than the approach without power-saving techniques, brownout-overbooking approach and auto-scaling approach, respectively, while ensuring Quality of Service.
机译:云数据中心的能耗一直是许多研究人员关注的主要问题,而云能耗巨大的原因之一在于计算资源的利用率低下。除了能耗之外,数据中心的另一个挑战是意外负载,这会导致过载和性能下降。与VM整合和动态电压频率缩放在整个数据中心过载时无法正常工作相比,通过动态停用应用程序可选组件(它们也被标识为容器/组件),电源不足已被证明是一种有前途的技术来处理过载和能耗。微服务。在这项工作中,我们提出了一种集成方法来管理基于容器的云数据中心中的能耗和电力不足。我们还在原型系统中评估了带有实际痕迹的建议调度策略。结果表明,与不采用节电技术,掉电超量预订方法和自动缩放方法的方法相比,我们的方法分别节省了40%,20%和10%的能量,同时确保了服务质量。

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