ML Traffic Shaping for Streaming and Offline Video Flows
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional network traffic shaping mechanisms struggle to accurately distinguish between streaming and offline video content, leading to improper shaping and poor user experience due to extended download times for offline content.
Innovation Solution
A machine learning-based framework that utilizes feature extraction and raw binary network data to categorize network flows, employing techniques like convolutional neural networks for accurate differentiation between streaming and offline content, enabling efficient traffic shaping operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional network traffic shaping mechanisms are used to control data flow, then bandwidth management is achieved, but accurate distinction between streaming and offline video content cannot be made
Solution Approach 1:
A machine learning classification system is introduced as an intermediary component between network traffic monitoring and traffic shaping decisions. This ML system analyzes network flows and classifies them as streaming or offline content, enabling accurate differentiation without directly modifying the core traffic shaping mechanisms. The intermediary ML layer processes complex network data and provides simplified classification results that guide traffic management decisions.
2Loss of time
If offline video content is downloaded using conventional mechanisms, then content availability is achieved, but extended download times occur
Solution Approach 1:
The system performs preliminary classification of network traffic as offline content before initiating the download process. By identifying offline video content in advance through machine learning analysis of network flows, the system can apply appropriate traffic shaping policies from the outset, prioritizing these downloads and allocating necessary bandwidth resources to complete downloads timely without delays.
3Productivity
If traffic shaping is applied to all video content uniformly, then network resources are managed, but video quality deteriorates during peak usage
Solution Approach 1:
The traffic shaping system applies different quality treatment to different content types based on local characteristics. By classifying network flows as either streaming or offline content, the system applies tailored shaping policies: streaming content receives policies optimized for continuous playback quality, while offline content receives policies optimized for complete download. This localized approach ensures each content type receives appropriate resource allocation, maintaining video quality during peak usage while managing overall network efficiency.
Data Source
AI summary
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.