Abstract: Aspects of the subject disclosure may include, for example, applying a model to a first unlabeled traffic item to generate a classification of the first unlabeled traffic item as a first class of traffic items or a second class of traffic items, generating a first score that is representative of a first confidence of the classification, determining that the first score is greater than a first threshold, and responsive to determining that the first score is greater than the first threshold, refining the model in accordance with at least the classification, resulting in a modified model. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device in which a processing system enables mobile devices on a communication network to perform latency tests to obtain first latency test data, determines an edge latency for each mobile device resulting in edge latencies, identifies a group of mobile device clusters according to the edge latencies, and identifies network edge server locations according to the group of mobile device clusters. The processing system also receives second latency test data from each of the network edge servers, and determines a data center latency for each of the network edge servers, resulting in a plurality of data center latencies. The processing system also identifies a data center location according to the data center latencies, and determines a network topology for the communication network; the network topology includes the data center location and the edge server locations. Other embodiments are disclosed.

Abstract: Concepts and technologies disclosed herein are directed collective intelligence-based cell congestion detection in mobile telecommunications networks. According to some aspects of the concepts and technologies disclosed herein, a core network element or device application can detect cell congestion based upon Internet layer, transport layer, and application layer data, such as, for example, traffic type, volume, rate, latency, jitter, and the like. According to one aspect disclosed herein, a cell congestion detection (“CCD”) system can collect data from an Internet layer, a transport layer, and an application layer. The CCD system can apply a machine learning algorithm to the data to determine whether the cell is congested. According to another aspect the CCD system can collect the data that is associated with a specific application executed by a plurality of UE devices connected to a cell.