Intelligent network probe placement optimization (e.g., using a computerized tool) is enabled. A method can comprise determining, from an inventory database, physical interfaces and service paths for data traffic to be monitored, according to a two-way active management protocol, with respect to network interfaces between cloud compute elements in a leaf-spine cloud fabric and radio access network equipment, based on the cloud compute elements, determining directed acyclic graph information representative of a directed acyclic graph of connections between the cloud compute elements and other cloud compute elements in the leaf-spine cloud fabric other than the cloud compute elements, and based on the directed acyclic graph information, determining a number and a distribution of probes to be employed at at least some of the physical interfaces and the service paths to monitor a parameter of the data traffic according to the two-way active management protocol.

Using key performance indicator (KPI) data sensed by vehicles is provided. A data server is programmed to receive, over a wide-area network from a plurality of vehicles, connectivity data of modems of the plurality of vehicles to the wide-area network, the connectivity data indicating KPI data, which road segment was being traversed when the KPI data was captured, and a time period during which the KPI data was captured. The data server is further programmed to identify outlier data elements in the KPI data using outlier detection criteria; and compile the KPI data per road segment and time period excluding the outlier data elements.

Provided is a method and system for optimizing utilization and performance of one or more unlicensed bands in a network (Wi-Fi and Citizens Broadband Radio Service (CBRS)) through a client device application that communicates with a central node deployed on a cloud platform. The method and system collects Radio Frequency (RF) measurements from a plurality of client devices and from a plurality of nearby point of attachments (PoAs) and transmits the collected RF measurements to the cloud platform. An optimal transmission channel/network in the one or more unlicensed bands is then calculated for one or more nearby PoAs using an Artificial Intelligence (AI) module based on the RF measurements, which utilizes a Dynamic Frequency Selection (DFS), an Automated Frequency Coordinator (AFC) and a Spectrum Access System (SAS) to select the optimal transmission channel/network based on the data-rate result received from the DFS, AFC and SAS.

Provided is a method and system for optimizing utilization and performance of one or more unlicensed bands in a network (Wi-Fi and Citizens Broadband Radio Service (CBRS)) through a client device application that communicates with a central node deployed on a cloud platform. The method and system collects Radio Frequency (RF) measurements from a plurality of client devices and from a plurality of nearby point of attachments (PoAs) and transmits the collected RF measurements to the cloud platform. An optimal transmission channel/network in the one or more unlicensed bands is then calculated for one or more nearby PoAs using an Artificial Intelligence (AI) module based on the RF measurements, which utilizes a Dynamic Frequency Selection (DFS), an Automated Frequency Coordinator (AFC) and a Spectrum Access System (SAS) to select the optimal transmission channel/network based on the data-rate result received from the DFS, AFC and SAS.

Techniques described herein relate to a method for managing performances of data protection services. The method may include obtaining subsystem statistics from subsystems; in response to obtaining the subsystem statistics, making a determination that the subsystem statistics indicate a concurrency adjustment; and in response to the determination, assigning an adjusted concurrency to the subsystems based on the subsystem statistics and priorities; and initiating performance of data protection services using the adjusted concurrency.

In one embodiment, a labeling service receives telemetry data for a cluster of endpoint devices in a first network environment. The endpoint devices in the cluster are clustered by a device classification service based on their telemetry data and labeled by a device type classifier of the device classification service as being of an unknown device type. The labeling service obtains a first device type label for the cluster of endpoint devices via a first user interface. The labeling service identifies one or more other network environments in which endpoint devices are located that have similar telemetry data as that of the cluster of endpoint devices. The labeling service obtains device type labels for the cluster of endpoint devices via a selected set of user interfaces from the identified one or more other network environments. The labeling service validates the first device type label for the cluster using the device type labels obtained via the selected set of user interfaces from the identified one or more other network environments.

Disclosed is a method for managing an application instance by a multi-access edge computing orchestrator (MEO) in a multi-access edge computing (MEC) system in a 5G or 6G communication system for supporting a higher data transmission rate, with the method including receiving, from an operational support system (OSS), an application instantiation request including a completion timer in which to finish the instantiation request; selecting at least one MEC platform manager (MEPM) for requesting the application instantiation; transmitting, to the selected MEPM, the application instantiation request; and transmitting, to the OSS, a response indicating that the application instantiation has failed, in case that the completion timer expires before receiving a configuration response from the selected MEPM.

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.

A method for anomaly detection and troubleshooting in a network includes parsing a network service descriptor (NSD) describing a network service (NS) to be deployed in the network. Monitoring data including time series of service-level metrics and resource-level metrics of network functions (NFs) of the NS are received from different domains of the network. Representations of the time series from the different domains are learned with a common dimensionality. An NS signature of the NS is computed as a cross-correlation matrix comprising cross-correlations between the service-level metrics and the resource-level metrics of the NFs. Embeddings of the NS signature are learned using a model and determining a reconstruction error of the model. It is determined whether the NS is anomalous based on the reconstruction error of the model. The NS is identified as a target for the troubleshooting in a case that the NS was determined to be anomalous.

In one embodiment, a device detects oscillations in a path metric for a network path between violating a service level agreement for an online application and not violating the service level agreement for the online application. The device classifies the oscillations as near-boundary or wild, based on degrees to which the path metric violated a threshold of the service level agreement. The device provides data regarding the oscillations to a user interface that includes an indication as to whether the oscillations are near-boundary or wild. The device adjusts the threshold of the service level agreement based in part on feedback from the user interface, when the oscillations are near-boundary.