Systems and methods for network traffic shaping for streaming and downloadable video
AssigneeVerizon Patent and Licensing Inc.
InventorsKhurram Abbas, Krzysztof Pawlikowski
Life of this patent
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Abstract
Disclosed are systems and methods that provide a computerized framework for network traffic shaping and optimization using a novel, trained machine learning (ML) algorithm. The disclosure provides for the designed framework to enhance the efficiency and quality of data transmission over a network by dynamically determining the appropriate delivery method—streaming or downloading—for various types of content. The disclosed framework can execute a trained ML algorithm that analyzes real-time network conditions and content characteristics, and the like, to make such determinations as to how to deliver content to users, for which content is then provided to requesting content provider applications executing on user devices.
Claims16
A method comprising:
- receiving a request for a user device to access content over a network, the content accessible via an application associated with the user device;
- analyzing the request, and selecting a machine learning (ML) model from a set of ML models, the ML model being a trained ML model for handling requests related to at least a type of the application, the ML model being trained using a set of operations, the set of operations comprising:
- collecting, over a time period, session data for a set of devices executing the application,
- analyzing the session data to determine a labeled training data set, the labeled training data set comprising information related to online and offline content, and
- training the ML model based on the labeled training data set, the training being performed in accordance with at least an efficiency threshold that corresponds to a speed at which the ML model can be executed at runtime;
- executing the ML model, the execution causing a network traffic shaping operation to be executed that corresponds to a type of the request;
- and
- providing, via the network traffic shaping operation, the user device access to the content in response to the request.
A system comprising:
- a processor configured to:
- receive a request for a user device to access content over a network, the content accessible via an application associated with the user device;
- analyze the request, and select a machine learning (ML) model from a set of ML models, the ML model being a trained ML model for handling requests related to at least a type of the application, the processor being configured to train the ML model using a set of operations, the set of operations comprising:
- collecting, over a time period, session data for a set of devices executing the application,
- analyzing the session data to determine a labeled training data set, the labeled training data set comprising information related to online and offline content, and
- training the ML model based on the labeled training data set, the training being performed in accordance with at least an efficiency threshold that corresponds to a speed at which the ML model can be executed at runtime;
- execute the ML model, the execution causing a network traffic shaping operation to be executed that corresponds to a type of the request;
- and
- provide, via the network traffic shaping operation, the user device access to the content in response to the request.
A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a processor, perform a method comprising:
- receiving a request for a user device to access content over a network, the content accessible via an application associated with the user device;
- analyzing the request, and selecting a machine learning (ML) model from a set of ML models, the ML model being a trained ML model for handling requests related to at least a type of the application, the ML model being trained using a set of operations, the set of operations comprising:
- collecting, over a time period, session data for a set of devices executing the application,
- analyzing the session data to determine a labeled training data set, the labeled training data set comprising information related to online and offline content, and
- training the ML model based on the labeled training data set, the training being performed in accordance with at least an efficiency threshold that corresponds to a speed at which the ML model can be executed at runtime;
- executing the ML model, the execution causing a network traffic shaping operation to be executed that corresponds to a type of the request;
- and
- providing, via the network traffic shaping operation, the user device access to the content in response to the request.
Dependencies are inferred from the claim text.