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Feedback control of server instances for right sizing in the cloud

机译:服务器实例的反馈控制,可在云中正确调整大小

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We consider a computing system based on summoning server instances on the fly, possibly from a remote cloud service. A feedback rule must be designed to track the exogenous load with the right service capacity, taking into account the inherent lags in server creation and deletion. We use fluid and diffusion approximations of queueing models to analyze control schemes that manage the tradeoff between job queueing and idle capacity, in the large scale limit. In particular we propose a method in which the system can achieve negligible queueing while minimizing idle capacity. Theoretical results are supported by simulations.
机译:我们考虑一个基于动态调用服务器实例的计算系统,可能来自远程云服务。考虑到服务器创建和删除过程中固有的滞后性,必须设计一个反馈规则来跟踪具有适当服务容量的外部负载。我们使用排队模型的流体近似和扩散近似来分析在大规模限制下管理作业排队和空闲容量之间权衡的控制方案。特别是,我们提出了一种方法,在该方法中,系统可以实现可忽略的排队,同时最大程度地减少空闲容量。理论结果得到了仿真的支持。

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