Cloud infrastructure planning systems and methods can utilize artificial intelligence/machine learning agents for developing a plan of demand, plan of record, plan of execution, and plan of availability for developing cloud infrastructure plans that are more precise and accurate, and that learn from previous planning and deployments. Some agents include one or more of supervised, unsupervised, and reinforcement machine learning to develop accurate predictions and perform self-tuning alone or in conjunction with other agents.

Aspects of the present disclosure involve systems and methods for a service activation system in a telecommunications network that utilizes one or more generic container files for building the configuration file to instantiate the service on the network. A request for service from a network may be received from an order entry system that includes specific information about the requested service. A collection of generic configuration files may be selected based on the information included in the service order and arranged to build a configuration file to be executed on the network. The service activation system may also include a component or group of components to verify a received service order and alter the service order with default information or data where applicable. The configuration file may also be executed on the network through one or more drivers communicating with the affected devices to configure the one or more network devices.

Techniques are described herein for generating and deploying network topologies to implement machine learning systems. A topology deployment system may receive data representing a logical model corresponding to a machine learning system, and may analyze the machine learning system to determine various components and attributes of the machine learning system to be deployed. Based on the components and attributes of the machine learning system, the topology deployment system may select target resources and determine constraints for the deployment of the machine learning system. A corresponding network topology may be generated and deployed across one or a combination of workload resource domains. The topology deployment system also may monitor and update the deployed network topology, based on performance metrics of the machine learning system and/or the current status of the system in a machine learning pipeline.

Techniques are described herein for generating and deploying network topologies to implement machine learning systems. A topology deployment system may receive data representing a logical model corresponding to a machine learning system, and may analyze the machine learning system to determine various components and attributes of the machine learning system to be deployed. Based on the components and attributes of the machine learning system, the topology deployment system may select target resources and determine constraints for the deployment of the machine learning system. A corresponding network topology may be generated and deployed across one or a combination of workload resource domains. The topology deployment system also may monitor and update the deployed network topology, based on performance metrics of the machine learning system and/or the current status of the system in a machine learning pipeline.

The embodiments herein disclose a method for managing machine learning (ML) services in a wireless communication network. The method includes: storing a plurality of ML packages, each executing a network service request; receiving a trigger based on the network service request from a server; determining a plurality of parameters corresponding to the network service request, on receiving the trigger from the server; determining an ML package based on the trigger and the plurality of parameters corresponding to the network service request; and deploying the determined at least one ML package for executing the network service request.

Systems, devices, and methods are discussed for memory efficient network use modeling.

A predefined hierarchical service tree can be stored that includes a top at a service category definition level and a bottom at a level of a number of devices, each of the number of devices selected to perform a specific service function. A sequential progression can be enforced through the predefined hierarchical service tree to perform a service.

A predefined hierarchical service tree can be stored that includes a top at a service category definition level and a bottom at a level of a number of devices, each of the number of devices selected to perform a specific service function. A sequential progression can be enforced through the predefined hierarchical service tree to perform a service.

This application relates to the field of communications technologies, and in particular, to a policy management method and system, and an apparatus. The method includes: requesting, by a policy decision entity, an NFVO in a management domain of a composite NS to perform a management operation on a policy group. According to the solution provided in this application, consistency between the LCM policy of the composite NS and the LCM policy of the nested NS forming the composite NS is ensured, and policy management execution efficiency is improved in a scenario of providing a composite NS across management domains.

This application relates to the field of communications technologies, and in particular, to a policy management method and system, and an apparatus. The method includes: requesting, by a policy decision entity, an NFVO in a management domain of a composite NS to perform a management operation on a policy group. According to the solution provided in this application, consistency between the LCM policy of the composite NS and the LCM policy of the nested NS forming the composite NS is ensured, and policy management execution efficiency is improved in a scenario of providing a composite NS across management domains.