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COMPUTING NODE FAILURE AND HEALTH PREDICTION FOR CLOUD-BASED DATA CENTER

机译:基于云的数据中心计算节点故障与健康预测

摘要

A system may include a node historical state data store having historical node state data, including a metric that represents a health status or an attribute of a node during a period of time prior to a node failure. A node failure prediction algorithm creation platform may generate a machine learning trained node failure prediction algorithm. An active node data store may contain information about computing nodes in a cloud computing environment, including, for each node, a metric that represents a health status or an attribute of that node over time. A virtual machine assignment platform may then execute the node failure prediction algorithm to calculate a node failure probability score for each computing node based on the information in the active node data store. As a result, a virtual machine may be assigned to a selected computing node based at least in part on node failure probability scores.
机译:系统可以包括具有历史节点状态数据的节点历史状态数据存储,包括在节点故障之前的时间段内表示节点的健康状态或节点属性的度量。节点故障预测算法创建平台可以生成机器学习训练节点故障预测算法。活动节点数据存储可以包含有关云计算环境中计算节点的信息,包括每个节点,包括表示该节点的每个节点的度量的度量。然后,虚拟机分配平台可以执行节点故障预测算法以基于活动节点数据存储中的信息计算每个计算节点的节点故障概率分数。结果,可以至少部分地基于节点故障概率分数将虚拟机分配给所选择的计算节点。

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