MULTI-PHASE CLOUD SERVICE NODE ERROR PREDICTION

Systems and techniques for multi-phase cloud service node error prediction are described herein. A set of spatial metrics and a set of temporal metrics may be obtained for node devices in a cloud computing platform. The node devices may be evaluated using a spatial machine learning model and a temporal machine learning model to create a spatial output and a temporal output. One or more potentially faulty nodes may be determined based on an evaluation of the spatial output and the temporal output using a ranking model. The one or more potentially faulty nodes may be a subset of the node devices. One or more migration source nodes may be identified from one or more potentially faulty nodes. The one or more migration source nodes may be identified by minimization of a cost of false positive and false negative node detection. 在此描述了用于多阶段云服务节点错误预测的系统和技术。针对云计算平台中的节点设备，可以获得空间度量集合和时间度量集合。可以使用空间机器学习模型和时间机器学习模型来评估节点设备，以创建空间输出和时间输出。可以基于使用排名模型对空间输出和时间输出的评估，来确定一个或多个潜在故障节点。一个或多个潜在故障节点可以是节点设备的子集。可以从一个或多个潜在故障节点标识一个或多个迁移源节点。可以