In one embodiment, a device in a network receives a switchover policy for a particular type of traffic in the network. The device determines a predicted effect of directing a traffic flow of the particular type of traffic from a first path in the network to a second path in the network. The device determines whether the predicted effect of directing the traffic flow to the second path would violate the switchover policy. The device causes the traffic flow to be routed via the second path in the network, based on a determination that the predicted effect of directing the traffic flow to the second path would not violate the switchover policy for the particular type of traffic.

Embodiments are generally directed apparatuses, methods, techniques and so forth to select two or more processing units of the plurality of processing units to process a workload, and configure a circuit switch to link the two or more processing units to process the workload, the two or more processing units each linked to each other via paths of communication and the circuit switch.

Disclosed herein are systems, methods, and storage media for network traffic projection and visualization. A computing system includes at least one circuit structured to receive network traffic data. A neural network is generated based on the network traffic data and includes a network traffic projection. The network traffic projection is displayed, via a graphical user interface, to a system administrator. In some embodiments, the computing system includes at least one circuit structured to receive change management data, such as application- and outage-related information. The change management data is combined with the network traffic projection data in a change window simulator, which recommends one or more change windows.

The disclosed system and method obtain a report of an issue reported by a user of the wireless telecommunication network, and historical information associated with the user and the wireless telecommunication network. The historical information includes multiple issues reported by users similar to the user, and multiple user statuses associated with the users similar to the user. The user status among the multiple user statuses includes active and inactive, indicating whether the user is an active member of the telecommunication network or has left the network. The system provides the historical information to an AI model, and obtains from the AI model a priority associated with the issue experienced by the user. The system causes a resolution of the issue based on the priority.

The disclosed system and method obtain a report of an issue reported by a user of the wireless telecommunication network, and historical information associated with the user and the wireless telecommunication network. The historical information includes multiple issues reported by users similar to the user, and multiple user statuses associated with the users similar to the user. The user status among the multiple user statuses includes active and inactive, indicating whether the user is an active member of the telecommunication network or has left the network. The system provides the historical information to an AI model, and obtains from the AI model a priority associated with the issue experienced by the user. The system causes a resolution of the issue based on the priority.

Disclosed are various embodiments for generating recommended replacement host machines for a datacenter. The recommendations can be generated based upon an analysis of historical workload usage across the datacenter. Clusters can be generated that cluster workloads together that are similar. Purchase plans can be generated based upon the identified clusters and benchmark data regarding servers.

A method of identifying faults in a utility supply network is disclosed. The method comprises identifying a first indication of a fault in the communications network based on a number of network performance queries received from users of user equipments (UEs) connected to the communications network within a first region of the communications network. The method further comprises identifying a second indication of a fault in the communications network based on network performance data associated with the first region. It is determined that a fault exists in the communications network based on identification of the first indication and the second indication.

Systems and methods for bandwidth optimization in a network include monitoring a state of the network, wherein the network is a connection-oriented network; utilizing analytics based on the monitoring to predict trends, create triggers, and determine updates to policy associated with the network; and performing bandwidth optimization on one or more connections based on the trends, the triggers, and the policy, wherein each of the one or more connections has one or more of a Wave Division Multiplexing (WDM) component, a Time Division Multiplexing (TDM) component, and a packet component, and wherein the bandwidth optimization finds the one or more connections with inefficient resource usages and moves the one or more connections, in one or more of time and space, to more optimal paths.

In one embodiment, a device receives, via a user interface, an indication of what is considered noise within a time series of a path performance metric. The device selects, based on the indication, a particular denoising filter to be applied to telemetry data obtained from one or more network paths regarding the path performance metric. The device forms model training data by applying the particular denoising filter to telemetry data obtained from one or more network paths regarding the path performance metric. The device trains, using the model training data, a prediction model to predict when a given network path will experience a failure condition.

In one embodiment, a device computes states of a network path associated with an online application by representing time series of telemetry data regarding the network path as discrete values. The device generates state trajectories from the states of the network path computed by the device. The device selects one or more sub-sequences of the state trajectories based on prediction performance metrics that represent how well the one or more sub-sequences are able to predict a failure condition of the network path. The device causes a networking entity to use the one or more sub-sequences of the state trajectories to perform predictive routing for the network path.