Cloud datacenters provide a backbone to our digital society. Inaccurate capacity procurement for cloud datacenters can lead to significant performance degradation, denser targets for failure, and unsustainable energy consumption. Although this activity is core to improving cloud infrastructure, relatively few comprehensive approaches and support tools exist for mid-tier operators, leaving many planners with merely rule-of-thumb judgement. We derive requirements from a unique survey of experts in charge of diverse datacenters in several countries. We propose Capelin, a data-driven, scenario-based capacity planning system for mid-tier cloud datacenters. Capelin introduces the notion of portfolios of scenarios, which it leverages in its probing for alternative capacity-plans. At the core of the system, a trace-based, discrete-event simulator enables the exploration of different possible topologies, with support for scaling the volume, variety, and velocity of resources, and for horizontal (scale-out) and vertical (scale-up) scaling. Capelin compares alternative topologies and for each gives detailed quantitative operational information, which could facilitate human decisions of capacity planning. We implement and open-source Capelin, and show through comprehensive trace-based experiments it can aid practitioners. The results give evidence that reasonable choices can be worse by a factor of 1.5-2.0 than the best, in terms of performance degradation or energy consumption.

中文翻译：

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Capelin：使用场景组合为云数据中心进行数据驱动的计算容量采购


云数据中心为我们的数字社会提供了支柱。云数据中心的容量采购不准确可能会导致性能显着下降、故障目标更加密集以及能源消耗不可持续。尽管这项活动是改善云基础设施的核心，但针对中型运营商的综合方法和支持工具相对较少，使得许多规划者只能凭经验判断。我们通过对多个国家负责不同数据中心的专家进行的独特调查得出要求。我们提出了 Capelin，一种用于中层云数据中心的数据驱动、基于场景的容量规划系统。 Capelin 引入了情景组合的概念，并在探索替代容量计划时利用了这一概念。在系统的核心，基于跟踪的离散事件模拟器能够探索不同的可能拓扑，支持缩放资源的数量、种类和速度，以及水平（横向扩展）和垂直（缩放）向上）缩放。 Capelin 比较了替代拓扑，并为每个拓扑提供了详细的定量操作信息，这可以促进容量规划的人类决策。我们实施并开源 Capelin，并通过全面的基于跟踪的实验证明它可以为从业者提供帮助。结果证明，在性能下降或能耗方面，合理的选择可能比最佳选择差 1.5-2.0 倍。