MACHINE-LEARNING INFUSED NETWORK TOPOLOGY GENERATION AND DEPLOYMENT

摘要

Techniques are described herein for deploying, monitoring, and modifying network topologies comprising various computing and network nodes deployed across multiple workload resource domains. A deployment system may receive operational data from a network topology deployed across multiple workload resource domains, such as public or private cloud computing environments, on-premise data centers, and the like. The operational data may be provided to a trained machine-learning model, and output from the trained model may be used, along with constraint inputs and resource inventories of the workload resource domains, to determine updated topology models which may be deployed within the workload resource domains.