Systems as described herein may include generating an aggregated service status report for a real-time service delivery platform. A plurality of services running in a service domain may be determined. A request for a status of system behavior corresponding to a particular service may be received. Service connection details of the particular service may be discovered and metric data of real-time data movement may be tracked. Real-time snapshot aggregation of the particular service may be provided. In a variety of embodiments, a real-time system behavior report for the service across availability zones may be presented.

Technologies for systems, methods and computer-readable storage media for solving complex distributed congestion in a 5G network by using traffic engineering data to redirect sessions. Specifically, involving anchoring a network node to a User Plane (UP) entity based on traffic engineering data and subscribing to traffic engineering data associated with requested parameters to push particular policies and/or select alternate application functions to correct congestion.

Various embodiments disclosed herein are related to an apparatus. In some embodiments, the apparatus includes a processor and a memory. In some embodiments, the memory includes instructions that, when executed by the processor, cause the apparatus to collect, at a cloud server, service data from a collector framework service of an edge network. In some embodiments, the memory includes instructions that, when executed by the processor, cause the apparatus to provide a configuration to the collector framework service based on the service data.

Some examples described herein relate to providing a customized cloud service. In an example, Key Service Indicators (KSI) may be received for a cloud service. The Key Service Indicators may be associated with a cloud service template for providing the cloud service. The resources required for providing the cloud service may be identified based on the Key Service Indicators.

Some examples described herein relate to providing a customized cloud service. In an example, Key Service Indicators (KSI) may be received for a cloud service. The Key Service Indicators may be associated with a cloud service template for providing the cloud service. The resources required for providing the cloud service may be identified based on the Key Service Indicators.

During operation, an electronic device receives, from a second electronic device in a network, a request for testing. In response, the electronic device set ups a video call with a video-call service. Then, the electronic device provides, to the second electronic device, an invitation for the video call. When the electronic device receives a notification (e.g., from the video-call service) that the video call has started, the electronic device provides content via the video-call service for the second electronic device. Next, the electronic device obtains communication-performance metrics associated with communication via the network during the video call and video-service performance metrics associated with the video call. Furthermore, the electronic device diagnoses a type of problem experienced at the second electronic device during the video call based at least in part on the communication-performance metrics, the video-service performance metrics and a pretrained machine-learning model.

A device may include a processor configured to determine a plurality of requirements for a Multi-Access Edge Computing (MEC) application requested by a customer; select a solution blueprint for the MEC application, from a set of solution blueprints, based on the determined plurality of requirements, wherein the solution blueprint includes an application deployment blueprint and a connectivity blueprint; and receive approval of the selected solution blueprint from the customer. The processor may be further configured to configure at least one transport network device based on the connectivity blueprint, in response to receiving the approval of the selected solution blueprint from the customer; and deploy at least one component of the MEC application on a MEC device in a MEC network based on the application deployment blueprint, in response to receiving the approval of the selected solution blueprint from the customer.

Embodiments of a device and method are disclosed. In an embodiment, a method for managing a network deployed at a customer site involves at a cloud server connected to the network, receiving wireless signal strength information that are detected by wireless sensors of wireless access points (APs) of the network deployed at the customer site; and at the cloud server, automatically disabling service-level agreement (SLA) scanning and probing of a first wireless sensor of a first wireless AP of the wireless APs when the wireless signal strength information indicates that wireless signal strength of each neighbor wireless AP of the first wireless AP is below a predefined threshold.

Embodiments of a device and method are disclosed. In an embodiment, a method for managing a network deployed at a customer site involves at a cloud server connected to the network, receiving wireless signal strength information that are detected by wireless sensors of wireless access points (APs) of the network deployed at the customer site; and at the cloud server, automatically disabling service-level agreement (SLA) scanning and probing of a first wireless sensor of a first wireless AP of the wireless APs when the wireless signal strength information indicates that wireless signal strength of each neighbor wireless AP of the first wireless AP is below a predefined threshold.

Systems and methods are disclosed for managing workload among server clusters is disclosed. According to certain embodiments, the system may include a memory storing instructions and a processor. The processor may be configured to execute the instructions to determine historical behaviors of the server clusters in processing a workload. The processor may also be configured to execute the instructions to construct cost models for the server clusters based at least in part on the historical behaviors. The cost model is configured to predict a processor utilization demand of a workload. The processor may further be configured to execute the instructions to receive a workload and determine efficiencies of processing the workload by the server clusters based at least in part on at least one of the cost models or an execution plan of the workload.