Abstract: This application provides a cloud system resource set recommendation method. After receiving a cloud system resource set recommendation request (401) triggered by a user and used to request to obtain cloud system resource sets that can support a target cloud service and meet a performance requirement, the recommendation apparatus selects, based on the cloud system resource set recommendation request (401), from the existing cloud system resource sets supporting the target cloud service, a target cloud system resource set meeting the performance requirement (402), and feeds back the target cloud system resource set to the user (403). The user does not need to select a required cloud system resource set from several limited resource sets with fixed configurations, but only needs to notify the recommendation apparatus of a target cloud service and a performance requirement that need to be supported by the cloud system resource set.

Abstract: This application provides a resource configuration prediction method and device applied to resource configuration prediction of a cloud service system, to improve resource allocation efficiency and reduce costs of the cloud service system. The method in embodiments of this application includes: obtaining original data of a cloud service system, where the original data includes running data of the cloud service system in a production environment or a production-like environment; obtaining training data based on the original data, and performing deep learning based on the training data to construct and obtain a resource configuration prediction model; when resource demand data input by a user is obtained, generating input data of the resource configuration prediction model based on the resource demand data; and performing prediction based on the input data and the resource configuration prediction model, to obtain resource configuration data of the cloud service system.

Abstract: The present invention relates to a server deployment method based on datacenter power management, wherein the method comprises: constructing a tail latency table and/or a tail latency curve corresponding to application requests based on CPU utilization rate data of at least one server; and determining an optimal power budget of the server and deploying the server based on the tail latency requirement of the application requests. By analyzing the tail latency table or curve, the present invention can, within the limitation of datacenter rated power, on the premise of ensuring the performance of latency-sensitive applications, maximise the deployment density of servers in data centers.