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Machine learning-based power capping and virtual machine placement in cloud platforms

机译：基于机器学习的电力盖和虚拟机放置在云平台中

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摘要

Systems and methods for machine learning-based power capping and virtual machine placement in cloud platforms are disclosed. A method includes applying a machine learning model to predict whether a request for deployment of a virtual machine corresponds to deployment of a user-facing (UF) virtual machine or a non-user-facing (NUF) virtual machine. The method further includes sorting a list of candidate servers based on both a chassis score and a server score for each server to determine a ranked list of the candidate servers, where the server score depends at least on whether the request for the deployment of the virtual machine is determined to be a request for a deployment of a UF virtual machine or a request for a deployment of an NUF virtual machine. The method further includes deploying the virtual machine to a server with highest rank among the ranked list of the candidate servers.
机译：公开了基于机器学习的电力覆盖和虚拟机放置的系统和方法。 一种方法包括应用机器学习模型来预测用于部署虚拟机的请求对应于用户面向用户的部署（UF）虚拟机或非用户面对（Nuf）虚拟机的部署。 该方法还包括基于机箱分数和每个服务器的服务器分数对候选服务器的列表进行排序，以确定候选服务器的排名列表，其中服务器分数至少取决于是否请求对虚拟的部署的请求 确定是用于部署UF虚拟机的请求或对NUF虚拟机的部署的请求。 该方法还包括将虚拟机部署到候选服务器的排名列表中具有最高等级的服务器。

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