A method of managing a controller of a software defined networking (SDN) network is implemented by a computing device in the SDN network. The method includes receiving status information for the controller, receiving usage information for the operating environment, generating at least one failure prediction for the controller based on the received status information, and outputting prediction information for the at least one failure prediction.

In one example, a location of a potential bottleneck of network traffic in a network is identified. Based on the location of the potential bottleneck, a first QoE inference model is selected from a plurality of respective QoE inference models. The respective QoE inference models are each trained to infer a respective QoE of the network traffic based on one or more respective network traffic metrics generated by monitoring the network traffic at a respective location in the network. One or more first network traffic metrics of the one or more respective network traffic metrics are generated by monitoring the network traffic at a first respective location. The one or more first network traffic metrics are provided to the first QoE inference model to infer a first respective QoE.

A synchronization method includes obtaining a first timestamp difference of a packet on a target link. The first timestamp difference is a difference between a sending timestamp and a receiving timestamp of the packet at a first moment. The synchronization method further includes performing packet selection based on the first timestamp difference to obtain a second timestamp difference; obtaining a delay prediction value of the target link at the first moment, compensating for the second timestamp difference based on the delay prediction value to obtain a compensated timestamp difference; and performing time and/or clock synchronization based on the compensated timestamp difference. The second timestamp difference is compensated for based on the delay prediction value, that is, PDV noise introduced to the target link is compensated for. In this way, the PDV noise is reduced.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a terminal idle time. In some implementations, a server can obtain communication data from a plurality of devices in a communication network, wherein the communication data indicates levels of network traffic for the device over time. The server can generate an idle period forecasting model configured to predict occurrence of future communication idle periods in which communication activity is predicted to be below a threshold. The server can provide the idle period forecasting model to each of the plurality of devices such that the devices can respectively use the idle period forecasting model to locally predict future communication idle periods of the devices.

A system and method for predicting rain fade for a rain zone using a deep learning system including a computer processor. The method may include: training a Neural Network (NN) by importing into the NN a training set of image information and beacon information, wherein the image information includes image datasets including of a cloud view of an Area of Interest (AoI), a geolocation and a timestamp, and the beacon information includes beacon datasets including a beacon strength, a current rain fade state, a geolocation and a timestamp; pre-processing to homogenize and to extract spatially and temporally matching data for the AoI from a live image information and a live beacon information; and forecasting a rain fade based on the data in a near-future. In the method, the geolocation of one or more of the beacon datasets is located within the AoI, and the periodicity of the live beacon information and the live image information is less than or equal to five (5) minutes.

System and methods for managing a Wi-Fi network of a plurality of Wi-Fi networks from a cloud-based Network Operations Control (NOC) dashboard are provided. A method, according to one implementations, includes the step of obtaining Wi-Fi metrics and cellular metrics from a network. The method also includes the step of displaying a dashboard on a user interface for use by a support agent at a Network Operations Center (NOC). Also, the method includes the step of displaying both the Wi-Fi metrics and the cellular metrics on the dashboard.

Service assurance is provided. A low priority pod corresponding to a low priority service in an orchestration platform that is to be evicted due to a predicted peak load period of a high priority service is identified based on analysis of historical and resource information. The low priority service corresponding to the low priority pod that is to be evicted due to the predicted peak load period of the high priority service is marked as an assured service for a guaranteed run in response to receiving an input from a user who was notified regarding eviction of the low priority pod. The low priority pod corresponding to the low priority service that is to be evicted due to the predicted peak load period of the high priority service is provisioned on a second host node prior to the eviction of the low priority pod from a first host node.

A method of enabling multi-connectivity in a wireless network includes predicting at least one of a RLF, a call drop, and a jitter based on a plurality of Key Performance Indicators (KPIs) associated with the UE and the wireless network, determining whether the UE is in one of a Dual Connectivity (DC) mode and a carrier aggregation (CA) mode, performing one of: adding a new secondary gNodeB (gNB) in response to determining that the UE is not in both the DC mode and the CA mode and converting the new gNB to a master gNB, converting an existing secondary gNodeB to a master gNodeB with one of an existing Master Cell Group (MCG) gNodeB and another gNodeB as the secondary gNodeB, in response to determining that the UE is in the DC mode, and converting an existing secondary cell to a primary cell.

A method of enabling multi-connectivity in a wireless network includes predicting at least one of a RLF, a call drop, and a jitter based on a plurality of Key Performance Indicators (KPIs) associated with the UE and the wireless network, determining whether the UE is in one of a Dual Connectivity (DC) mode and a carrier aggregation (CA) mode, performing one of: adding a new secondary gNodeB (gNB) in response to determining that the UE is not in both the DC mode and the CA mode and converting the new gNB to a master gNB, converting an existing secondary gNodeB to a master gNodeB with one of an existing Master Cell Group (MCG) gNodeB and another gNodeB as the secondary gNodeB, in response to determining that the UE is in the DC mode, and converting an existing secondary cell to a primary cell.

A method and a system for using a graph neural network framework to implement a link prediction in a multiplex network environment is provided. The method includes: identifying a plurality of layers of a multiplex network, each respective layer including a respective plurality of nodes; for each node included in at least a first layer, providing, by a structural node label and determining a common embedding across all of the plurality of layers and an individual embedding for each individual layer; using a k-nearest approach to select a subset of the plurality of layers for performing link prediction with respect to each layer based on the determined embeddings; and performing a link prediction by determining a respective feed-forward network with respect to each layer included in the selected subset.