Methods for managing a communication session in a communication network are disclosed. For example, a method includes detecting, by a first endpoint comprising at least one processor, an error condition associated with the communication session, sending, by the first endpoint, a notification of the error condition to a second endpoint that is using a transport layer session and receiving, by the first endpoint, a communication from the second endpoint, proposing a response to the error condition. Another method includes receiving, by a first endpoint comprising at least one processor, a notification of an error condition associated with the communication session, selecting, by the first endpoint, a response to the error condition, and sending, by the first endpoint, a communication to a second endpoint that is using a transport layer session, proposing a response to the error condition.

A troubleshooting method based on network function virtualization is provided, where the troubleshooting method may include: obtaining, by a first function management entity, fault information of a function entity; triggering, by the first function management entity, fault correlation processing according to the fault information, and formulating a troubleshooting policy according to a result of the fault correlation processing; and if the troubleshooting policy is formulated when troubleshooting time arrives, processing, by the first function management entity, a fault according to the troubleshooting policy; or if the troubleshooting policy is not formulated, processing, by the first function management entity, a fault according to a preset troubleshooting policy, where the preset troubleshooting policy is a policy formulated for a fault generated due to a reason of the function entity, so as to ensure that a service is not interrupted in a troubleshooting process, so that user experience is improved.

Presented herein are techniques performed in a network comprising a plurality of network nodes each configured to apply one or more service functions to traffic that passes the respective network nodes in a service path. At a network node, an indication is received of a failure or degradation of one or more service functions or applications applied to traffic at the network node. Data descriptive of the failure or degradation is generated. A previous service hop network node at which a service function or application was applied to traffic in the service path is determined. The data descriptive of the failure or degradation is communicated to the previous service hop network node.

In various examples, a system identifies a first issue object associated with the alert by making a first set of determinations, based on an alert of an active issue of a system resource. Additionally, the system can determine whether the active issue associated with the first issue object can be automatically corrected by one or more self-healing processes, based on the first issue object. Moreover, the system can implement the one or more self-healing processes, based on determining that the active issue associated with the first issue object can be automatically corrected by one or more self-healing processes.

A system may include sensor device comprising a sensor configured to measure sensor data indicating an operational parameter of industrial automation equipment associated with an industrial automation process. The system may also include communication circuitry configured to transmit the sensor data. Additionally, the system includes a processor configured to receive the sensor data. Further, the system includes a non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause the processor to perform operations including identifying an operational state of the industrial automation equipment based on the sensor data. The operations may also include determining a discrepancy between the sensor data and the operational state. Further, the operations may include modifying an operation of the processor from a first operational mode to a second operational mode of a plurality of operational based on the comparison.

Techniques for identifying and remedying performance issues of Virtualized Network Functions (VNFs) are discussed. An example method includes outputting a request to a network Element Manager (EM) to create a Virtualized Network Function (VNF) Performance Measurement (PM) job to collect VNF PM data from a VNF and receiving a set of VNF PM data associated with the VNF from the EM. The set of VNF PM data is processed associated with the VNF. A request to the EM is output to create a Virtualization Resource (VR) PM job to collect, through a VNF Manager (VNFM) and a virtualized infrastructure manager (VIM), VR PM data from a VR used by the VNF. Then a set of VR PM data is received from the EM and processed.

Techniques for identifying and remedying performance issues of Virtualized Network Functions (VNFs) are discussed. An example method includes outputting a request to a network Element Manager (EM) to create a Virtualized Network Function (VNF) Performance Measurement (PM) job to collect VNF PM data from a VNF and receiving a set of VNF PM data associated with the VNF from the EM. The set of VNF PM data is processed associated with the VNF. A request to the EM is output to create a Virtualization Resource (VR) PM job to collect, through a VNF Manager (VNFM) and a virtualized infrastructure manager (VIM), VR PM data from a VR used by the VNF. Then a set of VR PM data is received from the EM and processed.

In one embodiment, a supervisory service for a software-defined wide area network (SD-WAN) obtains telemetry data from one or more edge devices in the SD-WAN. The service trains, using the telemetry data as training data, a machine learning-based model to predict tunnel failures in the SD-WAN. The service receives feedback from the one or more edge devices regarding failure predictions made by the trained machine learning-based model. The service retrains the machine learning-based model, based on the received feedback.

A system receives, from one or more subsystems, one or more predicted outcomes associated with a device. The system provides provide at least a subset of the predicted outcomes as input to a machine learning model trained to identify a set of resolution actions. The system receives, from the machine learning model, the set of resolution actions for the subset of the predicted outcomes, wherein each resolution action in the set of resolution actions is associated with a probability of resolving at least one of the predicted outcomes in the subset of predicted outcomes. The system identifies a first resolution action from the set of resolution actions, wherein the first resolution action has a highest probability of resolving the at least one of the predicted outcomes in the subset of predicted outcomes. The system provides a first instruction to execute the first resolution action.

To improve the reliability of nodes that are utilized by a cloud computing provider, information about the entire lifecycle of nodes can be collected and used to predict when nodes are likely to experience failures based at least in part on early lifecycle errors. In one aspect, a plurality of failure issues experienced by a plurality of production nodes in a cloud computing system during a pre-production phase can be identified. A subset of the plurality of failure issues can be selected based at least in part on correlation with service outages for the plurality of production nodes during a production phase. A comparison can be performed between the subset of the plurality of failure issues and a set of failure issues experienced by a pre-production node during the pre-production phase. A risk score for the pre-production node can be calculated based at least in part on the comparison.