Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include receiving a deployment model selection of a software-defined-network (SDN) control service. The deployment model selection includes one of a centralized model, a decentralized model, a distributed model, or a hybrid model. The acts also can include deploying the SDN control service in the deployment model selection to control a physical computer network. The SDN control service uses a routing agent model trained using a reinforcement-learning model. Other embodiments are described.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include receiving information corresponding to a plurality of components in an information technology environment. The acts also can include determining a relationship between a first component of the plurality of components and a second component of the plurality of components based on the information, the relationship based on overlapping information corresponding to the first component and the second component. The acts additionally can include generating a marker for the relationship based on determining a threshold level of correlation between the information corresponding to the first component and the second component. The acts additionally can include auditing the relationship between the first component and the second component to determine if the marker is to be updated.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include generating a digital twin network simulation of a physical computer network controlled through a software-defined-network (SDN) control system. The acts also can include training a routing agent model on the digital twin network simulation using a reinforcement-learning model on traffic that flows through nodes of the digital twin network simulation. The routing agent model includes a machine-learning model. The acts additionally can include deploying the routing agent model, as trained, from the digital twin network simulation to the SDN control system of the physical computer network. Other embodiments are described.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include transmitting a user interface to be displayed to a user. The user interface can include one or more first interactive elements. The one or more first interactive elements display policy settings of a reinforcement learning model. The one or more first interactive elements are configured to allow the user to update the policy settings of the reinforcement learning model. The acts also can include receiving one or more inputs from the user. The inputs include one or more modifications of at least a portion of the one or more first interactive elements of the user interface to update the policy settings of the reinforcement learning model.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include receiving a deployment model selection of a software-defined-network (SDN) control service. The deployment model selection includes one of a centralized model, a decentralized model, a distributed model, or a hybrid model. The acts also can include deploying the SDN control service in the deployment model selection to control a physical computer network. The SDN control service uses a routing agent model trained using a reinforcement-learning model. Other embodiments are described.