A computer-implemented method includes training a machine-learning model, using a training dataset that distinguishes between critical systems and non-critical systems, to classify a particular computer system as critical or non-critical, wherein a label is applied to the particular computer system during the training that identifies the particular computer system as critical or non-critical, and wherein parameters that describe the critical systems or non-critical systems are used as features during the training. The method further includes receiving an input dataset that describes a plurality of computer systems in the enterprise environment. The method further includes outputting, using the trained machine-learning model, an identification of one or more critical systems of the plurality of computer systems within the enterprise environment and an identification of one or more non-critical systems of the plurality of computer systems within the enterprise environment, wherein each identification is associated with a confidence level.

A distributed cloud computing system is disclosed that includes a controller configured to deploy a first gateway in a first cloud computing network and a second gateway in a second cloud computing network and logic. The logic, upon execution by one or more processors, causes performance of operations including generating a topology mapping visualization illustrating a plurality of constructs and communication paths therebetween, wherein a first subset of the plurality of constructs are deployed in the first cloud computing network and a second subset of the plurality of constructs are deployed in the second cloud computing network, receiving user input corresponding to (i) a selection of one or more constructs and (ii) an identifier for the selection, generating a filtered topology mapping visualization of the selection of the one or more constructs and any connections therebetween, and causing rendering of the filtered topology mapping visualization on a display screen.

Systems and methods are disclosed to implement a time series anomaly detection system that uses configurable statistical control rules (SCRs) and a forecasting system to detect anomalies in a time series data (e.g. fluctuating values of a network activity metric). In embodiments, the system forecasts future values of the time series data along with a confidence interval based on seasonality characteristics of the data. The time series data is monitored for anomalies by comparing actual observed values in the time series with the predicted values and confidence intervals, according to the SCRs. The SCRs may be defined and tuned via a configuration interface that allows users to visually see how different SCRs perform over real data. Advantageously, the disclosed system allows users to create custom anomaly detection triggers for different types of time series data, without use of a monolithic detection model which can be difficult to tune.

UE capability for support of machine-learning (ML) based channel environment classification may be reported by a user equipment to a base station, where the channel environment classification classifies a channel environment of a channel between the UE and a base station based on one or more of UE speed or Doppler spread, UE trajectory, frequency selectivity or delay spread, coherence bandwidth, coherence time, radio resource management (RRM) metrics, block error rate, throughput, or UE acceleration. The user equipment may receive configuration for ML based channel environment classification, including at least enabling/disabling of ML based channel environment classification. When ML based channel environment classification is enabled, UE assistance information for ML based channel environment classification, and/or an indication of the channel environment (which may be a pre-defined channel environment associated with a lookup table), may be transmitted by the user equipment to the base station.

A method includes receiving network statistics from one or more vehicles that are members of a vehicular micro cloud. The method further includes detecting a degradation in vehicle-to-vehicle (V2V) communication performance based on the network statistics. The method further includes generating a communication graph based on the network statistics. The method further includes dividing the vehicular micro cloud into subgroups based on the communication graph.

A method for configuring output of information from an IoT network provides an interface for receiving user inputs from a user, the user inputs representing an asset type. User created rules define an asset behavior. The rules are input by the user in a human-readable language. Information representing the asset type and asset behavior is associated with one or more physical devices associated with a node of the network. The asset type is associated with a virtual area, which is associated with a physical area. The physical devices report raw state data of the asset, which is converted into user-defined state data and displayed to the user.

A controller device manages a plurality of network devices. The controller device includes a memory comprising a configuration database including a set of stored network device configurations, wherein each stored network device configuration of the set of stored network device configurations corresponds to a network device of the set of network devices. Additionally, the controller device includes processing circuitry configured to receive an intent file corresponding to an intended configuration for the set of network devices; receive a message from a network device of the set of network devices indicating an out-of-band configuration change at the network device; and determine, based on a stored network device configuration corresponding to the network device and an actual configuration of the network device, whether the intent file is compatible with the out-of-band configuration change.

A model-driven system automatically deploys a virtualized service, including multiple service components, on a distributed cloud infrastructure. A master service orchestrator causes a cloud platform orchestrator to retrieve a cloud services archive file, extract a cloud resource configuration template and create cloud resources at appropriate data centers as specified. The master service orchestrator also causes a software defined network controller to retrieve the cloud services archive file, to extract a cloud network configuration template and to configure layer 1 through layer 3 virtual network functions and to set up routes between them. Additionally, the master service orchestrator causes an application controller to retrieve the cloud services archive file, to extract a deployment orchestration plan and to configure and start layer 4 through layer 7 application components and bring them to a state of operational readiness.

A system and method for network planning with certain guarantees is disclosed. The system receives data characterizing various aspects of a backbone network, such as the nodes of the backbone network, how the nodes are connected by network links, the maximum available capacities of the network assets, network costs, and network asset reliability information. The system also receives data characterizing the requirements of different data communications, or flows, within the backbone network. For example, the backbone network may need to provide a flow a minimum amount of bandwidth or throughput, and the flow may have a minimum required uptime or availability. Based on the network data and flow data, the system generates a network plan that describes how capacity should be provided by different components of the network in a manner that guarantees satisfying flow requirements while balancing other considerations, such as network costs.

Parameters associated with a distributed network are received. A topology of a virtual network that corresponds to the distributed network is generated in view of the received parameters. The topology of the virtual network is configured to simulate the distributed network and a simulation of the distributed network is executed using the configured virtual network.