A method, system and apparatus are disclosed. According to one or more embodiments, a detection node in communication with a network function virtualization, NFV, system operating a NFV stack that is logically separable into a plurality of levels including a first level and a second level is provided. The detection node includes processing circuitry configured to: translate an executed first level event sequence to at least one translated second level event sequence, and compare the at least one translated second level event sequence to an executed second level event sequence to at least in part detect inconsistencies between the at least one translated second level event sequence and the executed second level event sequence where the executed second level event sequence and the executed first level event sequence being part of a multi-level sequence flow.

A method for providing cloud network reachability analysis includes receiving a reachability query requesting a reachability status of a target including a packet header associated with a data packet. The packet header includes a source IP address and a destination IP address. The method also includes generating one or more simulated forwarding paths for the data packet based on the packet header using a data plane model. Each simulated forwarding path includes corresponding network configuration information. The method includes determining the reachability status of the target based on the one or more simulated forwarding paths and providing the determined reachability status and the one or more simulated forwarding paths to a user device associated with the reachability query which causes the user device to present the network configuration information for each simulated forwarding path.

According to certain embodiments, a system comprises one or more processors and one or more computer-readable non-transitory storage media comprising instructions that, when executed by the one or more processors, cause one or more components of the system to perform operations comprising: receiving location data associated with a plurality of remote users accessing one or more existing remote access gateways that are located at one or more network locations; building a heatmap of user locations based at least in part on the received location data; and identifying, from the heatmap of user locations, at least one new network location in which to generate at least one new remote access gateway, or at least one existing network location in which to remove at least one of the existing remote access gateways.

An electronic device that assesses communication performance is described. During operation, the electronic device receives information specifying a location in an environment. For example, the information may correspond to user-interface activity associated with a user interface. Notably, the user interface may include an augmented reality and the user-interface activity may include defining the location, such as by dropping a pin in the augmented reality. Then, the electronic device provides the information to an access point and/or a controller of the access point, where the location is within communication range of the access point. Next, the electronic device receives, from the access point and/or the controller, measurements of one or more communication performance metrics at or proximate to the location during a time interval. Moreover, the electronic device provides a graphical representation of the communication performance at or proximate to the location based at least in part on the measurements.

Described herein are a device and a method for performing a network analysis. In one aspect, the device includes a reconfigurable neural network circuit to determine an indication of a predicted network characteristic. In one aspect, the reconfigurable neural network circuit includes a control circuit to select a packet attribute or a flow attribute of a raw packet stream from a pipeline, and determine a configuration setting corresponding to the packet attribute or the flow attribute. The configuration setting may indicate a configuration of the reconfigurable neural network circuit to implement a neural network. In one aspect, the reconfigurable neural network circuit includes a storage to provide neural network parameters of the neural network, according to the configuration setting. In one aspect, the reconfigurable neural network circuit includes computational circuits to perform computations based on the neural network parameters from the storage to determine the indication of the predicted network characteristic.

Techniques for implementing rollback of infrastructure changes in an infrastructure orchestration service are described. In certain examples, an infrastructure orchestration service is disclosed that manages both provisioning and deploying of infrastructure assets within a cloud environment. The service receives a plan comprising a set of instructions associated with a set of infrastructure assets of an execution target and identifies a first state of the set of infrastructure assets. The service executes the set of instructions in the plan to achieve a second state for the set of infrastructure assets. Based in part on the executing, the service receives a trigger for rolling back the plan to restore the set of infrastructure assets in the plan to the first state and executes a rollback plan for the plan. The service then transmits a result associated with the execution of the rollback plan.

Disclosed are systems, methods, and computer-readable media for assuring tenant forwarding in a network environment. Network assurance can be determined in layer 1, layer 2 and layer 3 of the networked environment including, internal-internal (e.g., inter-fabric) forwarding and internal-external (e.g., outside the fabric) forwarding in the networked environment. The network assurance can be performed using logical configurations, software configurations and/or hardware configurations.

This application provides a network configuration method, apparatus, and system. The method includes: determining, based on a mapping relationship, that a first data node in a first YANG data model corresponds to a second data node in a second YANG data model, where the first data node and the second data node include a same indication operation, and the mapping relationship includes a correspondence between a data node in the first YANG data model and a data node in the second YANG data model; and generating a first packet based on the second data node.

Disclosed are methods, systems, and non-transitory computer-readable medium for a contextual transformation of an analytical model for an industrial internet of things (IIoT) edge node. For instance, the method may include receiving the analytical model from a cloud service; obtaining local data of the IIoT edge node; analyzing the local data to determine a situational context of the IIoT edge node; determining whether to transform the analytical model based on a fit between the analytical model and the situational context; and in response to determining to transform the analytical model, transforming the analytical model based on the situational context to derive a transformed analytical model.

Disclosed herein is a system for automating the causality detection process when upgrades are deployed to different resources that provide a service. The resources can include physical and/or virtual resources (e.g., processing, storage, and/or networking resources) that are divided into different, geographically dispersed, resource units. To determine whether a root cause of a problem is associated with an upgrade event that has recently been deployed, a system is configured to use telemetry data to compute an upgrade-to-upgrade score that represents differences between two different upgrade events that are deployed to the same resource unit. The system is further configured to use telemetry data to compute an upgrade unit-to-unit score that represents differences between the same upgrade event being deployed to two different resource units. The scores can be used to output an alert, for an analyst, that signals whether a recently deployed upgrade event is the cause of a problem.