Abstract: A parameter configuration method includes: receiving a configuration parameter of a service system and a service objective of the service system through a standard interface, performing parameter optimization on the configuration parameter to obtain a recommended parameter value that meets the service objective, and then outputting the recommended parameter value to implement parameter configuration for the service system. The parameter configuration method abstracts parameter configuration problems in different service scenarios into a mathematical problem of inputting and expression through a standard interface. In this way, a universal parameter configuration method is provided without case-by-case parameter configuration. In addition, an expert in the service field can independently complete parameter configuration without artificial intelligence experience.

Abstract: A method includes obtaining a life cycle of at least one node (configured to deploy the pod) in the service cluster and a life cycle of at least one pod; then, determining a target node based on the life cycle of the at least one node and the life cycle of the at least one pod, where the target node is a node on which the pod is to be deployed or a node from which the pod is to be deleted; and next, scaling, by the container management system, the pod on the target node. According to the method, the pod is scaled with reference to the life cycle of the node in the service cluster and the life cycle of the to-be-deployed pod or the deployed pod.

Abstract: This application provides a parameter configuration method, including: receiving a configuration parameter of a service system and a service objective of the service system through a standard interface, performing parameter optimization on the configuration parameter to obtain a recommended parameter value that meets the service objective, and then outputting the recommended parameter value, to implement parameter configuration for the service system. The method abstracts parameter configuration problems in different service scenarios into a mathematical problem of inputting and expression through a standard interface. In this way, a universal parameter configuration method is provided, without case-by-case parameter configuration. In addition, an expert in the service field can independently complete parameter configuration without artificial intelligence experience, thereby improving configuration efficiency and reducing configuration costs.