A system described herein may provide a technique for selecting and configuring a capture component and a filtering and analysis component for traffic capture in a manner that suitably accounts for Service Level Agreements (“SLAs”) of traffic to be captured. A traffic capture request may indicate one or more SLAs and/or other attributes, and an orchestration system may identify a network function (such as a User Plane Function (“UPF”) of a wireless network) that handles traffic that includes the requested traffic and instantiate the capture component at a node that implements the network function. The orchestration system may select a node, from a cluster of nodes, that is able to filter and analyze the traffic in an expedient manner in accordance with SLA(s) associated with the requested traffic, and install the filtering and analysis component at the selected node.

A method for dynamic incident management for a computer infrastructure includes: receiving, via a network interface, end-user experience data associated with a plurality of end-user devices; storing the end-user experience data for a corresponding end-user device in an infrastructure database node corresponding to a location of the of the end-user device; upon determining that the end-user experience data for the corresponding end-user device indicates a latency issue at the end-user device, traversing the corresponding infrastructure database node to determine whether a threshold number of latency issues have occurred at the location within a defined time period; when the threshold number of latency issues have occurred, mapping the latency issues to a system performance alert received within a mapping time threshold as a potential root cause; and triggering the execution of response instructions based on the potential root cause.

One exemplary aspect describes systems and methods for determining normal SLE behavior, determining when a SLE exhibits abnormal deterioration, and determining whether to take an action to mitigate what appears to be an indication of an abnormal SLE.

In one embodiment, a network assurance service that monitors a wireless network receives data regarding connection failures of a wireless client of the wireless network. The network assurance service forms a behavioral profile for the wireless client based on the received data regarding the connection failures of the wireless client. The network assurance service uses machine learning to determine whether the behavioral profile of the wireless client is an outlier in relation to behavioral profiles of other wireless clients of the wireless network. The network assurance service causes performance of a mitigation action with respect to the wireless client, when the wireless client is determined to be an outlier.

Systems and methods are described for monitoring performance and allocating resources to improve the performance of a wireless telecommunications network. A wireless telecommunications network may be comprised of base stations and other infrastructure equipment, which may be sourced from various suppliers. Users may generate traffic on the wireless network, and performance metrics relating to the user experience may be collected from individual base stations. The set of available metrics for a particular base station may vary according to the supplier. A machine learning model is thus trained using metrics from multiple base stations, and used to estimate values for metrics that a particular base station does not provide. The metrics are then further characterized according to the service classes of the users, and resources for improving the performance of base stations are allocated according to the reported and estimated metrics for various service classes.

A system for network design comprising: a controller in communication with an input/output device, the controller being in communication with at least one fiber infrastructure database including at least one of a link performance data and a service volume data and a performance risk database; a modeling component configured to predict a number of potential customers impacted by a loss of a span and a number of circuits; wherein the controller is in communication with the modeling component, the controller being configured to merge data from the fiber infrastructure database and performance risk database to provide at least one of a recommended route design and an automated order to define a new route.

Evaluation of a plurality of activities comprising a service includes, for each of the plurality of activities, identification of a network service path of the activity from the source to the user, wherein a network service path of a first activity of the plurality of activities is different from a network service path of at least one other of the plurality of activities, measurement of a service quality metric for each respective network segment of each identified network service path, and, for each of the plurality of activities, determination of a service quality metric based on the service quality metrics measured for each of the network segments of the network service path of the activity.

Techniques for managing service requests in cloud computing systems are disclosed herein. In one embodiment, a method can include in response to determining that a profile of detected service requests substantially deviates from the predetermined profile, identifying a component failure of a service component in the cloud computing system by searching a database containing operational event logs of the cloud computing system based on the detected service requests. The method can then include posting, on a user portal of the cloud computing system, a notification to additional users regarding the identified component failure of the service component and performing various remedial actions to correct the identified component failure.

Systems and methods for automation of a customer support sorting process are described. A probability that a call from a potential caller will be received by a call system may be determined based at least in part on historical status information and call history information. The historical status information may indicate one or more past operational states of a service network. The call history information may indicate one or more calls received by the call system, as well as a subject matter of each of the one or more calls. If the determined probability satisfies a threshold, a notification to the potential caller may be transmitted. The notification may inform the potential caller of a current operational state of the service network.

Systems and methods are described for monitoring performance and allocating resources to improve the performance of a wireless telecommunications network. A wireless telecommunications network may be comprised of base stations and other infrastructure equipment, which may be sourced from various suppliers. Users may generate traffic on the wireless network, and performance metrics relating to the user experience may be collected from individual base stations. The set of available metrics for a particular base station may vary according to the supplier. A machine learning model is thus trained using metrics from multiple base stations, and used to estimate values for metrics that a particular base station does not provide. The metrics are then further characterized according to the service classes of the users, and resources for improving the performance of base stations are allocated according to the reported and estimated metrics for various service classes.