Systems, methods and/or computer program products for managing and dynamically automating service mesh communications between microservices, eliminating unnecessary exposure of microservice ports and increasing security between microservices of the service mesh. The control plane collects data describing communications between microservices and tracks the frequency at which microservices communicate. Collected data is fed to machine learning models which outputs a forecast predicting future communication interactions between microservices. Using the predicted requirements for facilitating communications between microservices of the service mesh, an allowed list of communications can be generated describing the microservices allowed to send and receive communications, duration of communications allowed, when such communications are allowed, and the ports that will be used for facilitating the communication between microservices. Administrators of the service mesh may manually override the one or more approved aspects of the dynamically generated allowed list configured automatically by the service mesh.

Techniques are described for monitoring application performance in a computer network. For example, a network management system (NMS) includes a memory storing path data received from a plurality of network devices, the path data reported by each network device of the plurality of network devices for one or more logical paths of a physical interface from the given network device over a wide area network (WAN). Additionally, the NMS may include processing circuitry in communication with the memory and configured to: determine, based on the path data, one or more application health assessments for one or more applications, wherein the one or more application health assessments are associated with one or more application time periods for a site, and in response to determining at least one failure state, output a notification including identification of a root cause of the at least one failure state.

Methods, systems, and devices for wireless communications are described. Autonomous transmissions between a user equipment (UE) and a base station may be configured that include at least one of a modulation and coding scheme (MCS) or resources for the transmissions. In some cases, a trigger may be detected that changes the MCS or resources to be used for the autonomous transmissions. The trigger may include the presence or absence of retransmissions or the value of a channel measurement falling below or exceeding a threshold value. Accordingly, the base station and UE may adjust the MCS or resources to be used for the autonomous transmissions based on detecting the trigger and then communicate using the adjusted MCS or resources. In some cases, the configuration for the autonomous transmissions may be signaled via a medium access control (MAC) control element (CE).

A method for automated web resource deployment is provided. The method comprises creating web resource publication requests, wherein each web resource publication request comprises a number of configuration changes necessary to publish a web resource, on a network, at a particular uniform resource location. A standard format, validation workflow, and an approval workflow are provided for automation of the web resource publication requests. Once validated and approved, web resource publication requests are automatically converted to API calls which are executed on backend servers to implement the configuration changes required in the environment without further human intervention.

A system and method for dynamically creating distributed, self-adjusting and optimizing core network with machine learning is disclosed. The method includes receiving a request to access one or more services and establishing a secure real time communication session with one or more client devices and a set of service layers based on the received request. The method further includes determining one or more service parameters based on the received request and sending one or more handshake messages to each of the set of service layers. Further, the method includes determining one or more environmental parameters and determining best possible service layer capable of processing the received request by using a trained service based ML model. The method includes processing the request at the determined best possible service layer and terminating or transferring the secure real time communication session after the request is processed.

Embodiments are described herein for systems and methods for continuously monitoring a network structure of one or more networks using a cloud-based network monitoring system, and rearranging, using the cloud-based network monitoring system, the network structure of the one or more networks to protect confidential and/or prioritized assets of the one or more networks based at least in part on the monitoring of the network structure of the one or more networks. In certain embodiments, the cloud-based network monitoring system is configured to continuously monitor a network structure of one or more networks, and to automatically rearrange the network structure of the one or more networks to protect confidential and/or prioritized assets of the one or more networks based at least in part on the continuous monitoring of the network structure of the one or more networks.

Methods are provided for modifying assurance monitoring of a service based on operational states. The methods involve establishing, based on service configuration information, an assurance monitoring for a service provided by a plurality of network nodes that establish network connectivity for the service. The service includes a plurality of sub-services. The methods further involve obtaining, from the plurality of network nodes, telemetry data related to the service, determining one or more operational states of the plurality of network nodes based on the telemetry data, and modifying the assurance monitoring for the service based on the one or more operational states of the plurality of network nodes.

The present technology provides a system, method and computer-readable medium for configuration pattern recognition and inference, directed to a device with an existing configuration, through an extensible policy framework. The policy framework uses a mixture of python template logic and CLI micro-templates as a mask to infer the intent behind an existing device configuration in a bottom-up learning inference process. Unique values for device/network identifiers and addresses as well as other resources are extracted and accounted for. The consistency of devices within the fabric is checked based on the specific policies built into the extensible framework definition. Any inconsistencies found are flagged for user correction or automatically remedied by a network controller. This dynamic configuration pattern recognition ability allows a fabric to grow without being destroyed and re-created, thus new devices with existing configurations may be added and automatically configured to grow a Brownfield fabric.

A control node can be automatically deployed at a remote location according to some examples described herein. In one example, a system can automatically set up a control node at a remote location by performing various operations. The operations can include interacting with the remote location to deploy an instance of the control node at the remote location. The operations can include providing a configuration script to the remote location for use by the instance in configuring one or more managed nodes. The operations can include providing connection information to the remote location for use by the instance in establishing a network connection to the one or more managed nodes. The system can then initiate a configuration process in which the control node establishes the network connection to the one or more managed nodes and then configures the one or more managed nodes in accordance with the configuration script.

A method of provisioning a network may include, with a network controller, identifying a first network intent of a computing network based at least in part on an execution of a user interface (UI) or API layer at a client device, and identifying a modification of at least one object within the first network intent within the UI or API layer at the client device as the first network intent is being modified. The modification defines a delta between the first network intent and a second network intent. The method may further include, with a provisioning service executed by the network controller, receiving the delta as a payload from the client device, and provisioning at least one computing device within the computing network based at least in part on the delta. The method further includes automatically modifying the at least one object based on the received delta, including a further modification of the second network intent.