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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Capelin: Data-Driven Compute Capacity Procurement for Cloud Datacenters Using Portfolios of Scenarios
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Capelin: Data-Driven Compute Capacity Procurement for Cloud Datacenters Using Portfolios of Scenarios

机译：Capelin：使用场景组合的云数据中心的数据驱动计算能力采购

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摘要

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.
机译：云数据中心提供了我们的数字社会的骨干。云数据中心的不准确的能力采购可以导致显着的性能下降，失败的密集目标和不可持续的能耗。虽然这项活动是提高云基础设施的核心，但中级运营商存在相对较少的综合方法和支持工具，留下了许多策划者，仅限拇指判断。我们从几个国家/地区的各种数据中心的专家调查中获得了要求。我们提出了Capelin，一个用于中级云数据中心的数据驱动的基于场景的容量规划系统。 Capelin介绍了情景组合的概念，它利用其探索替代能力计划。在系统的核心，基于轨迹的离散事件模拟器可以探索不同可能的拓扑，并支持缩放资源的音量，品种和速度，以及水平（横向输出）和垂直（比例-up）缩放。 Capelin比较替代拓扑，每次提供详细的定量运营信息，这可以促进人为规划的决定。我们实施和开源卡布林，并通过其可以帮助从业者的全面轨迹实验表明。结果证明，就性能下降或能源消耗而言，合理的选择可能比最佳倍数为1.5-2.0。

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