Abstract: A method includes obtaining a first plurality of encrypted traffic flows traversing a communication network, performing a first classification, wherein a result of the first classification identifies a traffic type associated with each encrypted traffic flow of the first plurality of encrypted traffic flows, and wherein the first classification is based on a traffic pattern of the each encrypted traffic flow, performing a second classification, wherein a result of the second classification identifies a traffic type associated with each server name indication from which the first plurality of encrypted traffic flows is associated, and wherein the second classification is based on the result of the first classification, and performing a third classification identifying a traffic type associated with each encrypted traffic flow of the first plurality of encrypted traffic flows, wherein the third classification is based on a combination of the results of the first classification and the second classification.

Abstract: A prediction system of an online system deploys one or more machine-learned architectures to generate predictions. In one embodiment, the machine-learned architecture is a stacked ensemble model. The stacked ensemble model includes a plurality of base models, where a base model is coupled to receive input data and generate a base prediction for the input data. The stacked ensemble model includes a meta model that combines the base predictions to generate a meta prediction for the input data. The prediction system also generates a reliability measure that takes advantage of the base predictions to evaluate the reliability of the meta prediction. In this manner, while the quality of individual predictions may differ from one another depending on the values of the input data, the prediction system can dynamically generate the reliability measure to account for this variation.

Abstract: Aspects of the subject disclosure may include, for example, receiving a first plurality of operating parameters associated with a first plurality of communication devices, obtaining a first prediction of a performance of a first communication device included in the first plurality of communication devices that is based on an application of the first plurality of operating parameters to a model, and responsive to a determination that the first prediction of the performance is inadequate in accordance with a first threshold, transmitting a first command to at least one communication device included in the first plurality of communication devices to modify at least one operating parameter of the at least one communication device. Other embodiments are disclosed.

Abstract: A method includes obtaining a first plurality of encrypted traffic flows traversing a communication network, performing a first classification, wherein a result of the first classification identifies a traffic type associated with each encrypted traffic flow of the first plurality of encrypted traffic flows, and wherein the first classification is based on a traffic pattern of the each encrypted traffic flow, performing a second classification, wherein a result of the second classification identifies a traffic type associated with each server name indication from which the first plurality of encrypted traffic flows is associated, and wherein the second classification is based on the result of the first classification, and performing a third classification identifying a traffic type associated with each encrypted traffic flow of the first plurality of encrypted traffic flows, wherein the third classification is based on a combination of the results of the first classification and the second classification.

Abstract: Aspects of the subject disclosure may include, for example, a method, including identifying recommended video clips for a user of a communication device according to a video viewing profile and video subject matter information associated with a plurality of video clips, identifying an edge cloud server for facilitating network access by the communication device at a location, determining a reduced activity period for a data path between a video content server and the edge cloud server by comparing activity information for the data path and an activity threshold, directing the video content server to store the recommended video clips at the edge cloud server during the reduced activity period, and providing a listing of the recommended video clips to an application, where the communication device receives a video clip from the edge cloud server responsive to a selection of the video clip via the application. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method, including identifying recommended video clips for a user of a communication device according to a video viewing profile for the user and video subject matter information associated with a plurality of video clips, identifying an edge cloud server for facilitating network access by the communication device at a location, determining a reduced activity period for a data path between a video content server and the edge cloud server by comparing activity information for the data path and an activity threshold, directing the video content server to store the recommended video clips at the edge cloud server during the reduced activity period, and providing a listing of the recommended video clips to an application, where the communication device receives a video clip of the recommended video clips from the edge cloud server responsive to a selection of the video clip via the application. Other embodiments are disclosed.

Abstract: Methods, computer readable media, and devices for determining a hyperparameter for influencing non-local samples in machine learning are disclosed. One method may include identifying a set of local samples associated with a first entity, identifying a set of non-local samples comprising samples associated with a plurality of entities other than the first entity, assigning a local sample weight to one or more samples of the set of local samples, determining a range of non-local sample weights, determining a range of hyperparameters based on the range of non-local sample weights, determining an optimized hyperparameter based on the range of hyperparameters, assigning an optimized non-local sample weight to one or more samples of the set of non-local samples, and generating a prediction using machine learning.

Abstract: A method includes obtaining a first plurality of encrypted traffic flows traversing a communication network, performing a first classification, wherein a result of the first classification identifies a traffic type associated with each encrypted traffic flow of the first plurality of encrypted traffic flows, and wherein the first classification is based on a traffic pattern of the each encrypted traffic flow, performing a second classification, wherein a result of the second classification identifies a traffic type associated with each server name indication from which the first plurality of encrypted traffic flows is associated, and wherein the second classification is based on the result of the first classification, and performing a third classification identifying a traffic type associated with each encrypted traffic flow of the first plurality of encrypted traffic flows, wherein the third classification is based on a combination of the results of the first classification and the second classification.

Abstract: A processing system including at least one processor may generate a binary traffic vector from a traffic flow in a communication network. The binary traffic vector may comprise a plurality of elements, each associated with a respective time period and comprising one of: a first value or a second value. For each time period the traffic flow comprises a transfer of a data unit, a corresponding element comprises the first value, and for time period the traffic flow does not comprise a transfer of a data unit, a corresponding element comprises the second value. The processing system may then apply a traffic flow record comprising the binary traffic vector as an input to a deep learning classifier trained to classify traffic flow records into traffic categories, and determine a traffic category from an output of the deep learning classifier in accordance with the traffic flow record as the input.

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, 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, receiving a first plurality of operating parameters associated with a first plurality of communication devices, obtaining a first prediction of a performance of a first communication device included in the first plurality of communication devices that is based on an application of the first plurality of operating parameters to a model, and responsive to a determination that the first prediction of the performance is inadequate in accordance with a first threshold, transmitting a first command to at least one communication device included in the first plurality of communication devices to modify at least one operating parameter of the at least one communication device. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method, including identifying recommended video clips for a user of a communication device according to a video viewing profile for the user and video subject matter information associated with a plurality of video clips, identifying an edge cloud server for facilitating network access by the communication device at a location, determining a reduced activity period for a data path between a video content server and the edge cloud server by comparing activity information for the data path and an activity threshold, directing the video content server to store the recommended video clips at the edge cloud server during the reduced activity period, and providing a listing of the recommended video clips to an application, where the communication device receives a video clip of the recommended video clips from the edge cloud server responsive to a selection of the video clip via the application. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving a first plurality of operating parameters associated with a first plurality of communication devices, obtaining a first prediction of a performance of a first communication device included in the first plurality of communication devices that is based on an application of the first plurality of operating parameters to a model, and responsive to a determination that the first prediction of the performance is inadequate in accordance with a first threshold, transmitting a first command to at least one communication device included in the first plurality of communication devices to modify at least one operating parameter of the at least one communication device. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method, including identifying recommended video clips for a user of a communication device according to a video viewing profile for the user and video subject matter information associated with a plurality of video clips, identifying an edge cloud server for facilitating network access by the communication device at a location, determining a reduced activity period for a data path between a video content server and the edge cloud server by comparing activity information for the data path and an activity threshold, directing the video content server to store the recommended video clips at the edge cloud server during the reduced activity period, and providing a listing of the recommended video clips to an application, where the communication device receives a video clip of the recommended video clips from the edge cloud server responsive to a selection of the video clip via the application. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving a first plurality of operating parameters associated with a first plurality of communication devices, obtaining a first prediction of a performance of a first communication device included in the first plurality of communication devices that is based on an application of the first plurality of operating parameters to a model, and responsive to a determination that the first prediction of the performance is inadequate in accordance with a first threshold, transmitting a first command to at least one communication device included in the first plurality of communication devices to modify at least one operating parameter of the at least one communication device. Other embodiments are disclosed.

Abstract: Concepts and technologies disclosed herein are directed to 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.

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: Aspects of the subject disclosure may include, for example, a method, including identifying recommended video clips for a user of a communication device according to a video viewing profile for the user and video subject matter information associated with a plurality of video clips, identifying an edge cloud server for facilitating network access by the communication device at a location, determining a reduced activity period for a data path between a video content server and the edge cloud server by comparing activity information for the data path and an activity threshold, directing the video content server to store the recommended video clips at the edge cloud server during the reduced activity period, and providing a listing of the recommended video clips to an application, where the communication device receives a video clip of the recommended video clips from the edge cloud server responsive to a selection of the video clip via the application. Other embodiments are disclosed.

Abstract: A method includes obtaining a first plurality of encrypted traffic flows traversing a communication network, performing a first classification, wherein a result of the first classification identifies a traffic type associated with each encrypted traffic flow of the first plurality of encrypted traffic flows, and wherein the first classification is based on a traffic pattern of the each encrypted traffic flow, performing a second classification, wherein a result of the second classification identifies a traffic type associated with each server name indication from which the first plurality of encrypted traffic flows is associated, and wherein the second classification is based on the result of the first classification, and performing a third classification identifying a traffic type associated with each encrypted traffic flow of the first plurality of encrypted traffic flows, wherein the third classification is based on a combination of the results of the first classification and the second classification.