ML-Based Interface Selection for NAT Path Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In network environments using network address translation, existing path selection methods like ICE struggle to efficiently choose transport addresses, leading to impairments such as jitter, delay, and packet drops, which degrade real-time communication quality.
Innovation Solution
A machine-learning-based interface selection process that analyzes candidate path information against training data to produce path recommendation information, optimizing the selection of transport addresses for communication sessions by classifying paths based on quality metrics like jitter, latency, and packet drops, and adapting priority calculations using historical data and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing path selection methods like ICE are used in NAT environments, then path selection can be performed, but the selection efficiency is poor leading to jitter, delay and packet drops
Solution Approach 1:
The system performs preliminary path quality assessment by analyzing candidate path information against training data before final path selection. The machine learning model pre-evaluates multiple candidate paths using historical performance data, predicting which paths will deliver best communication quality. This preliminary analysis enables the system to prioritize testing and selection of high-quality paths, reducing the number of failed connectivity checks and improving overall path selection efficiency while maintaining reliable communication quality
Solution Approach 2:
The system implements feedback mechanisms where path selection decisions are continuously improved using observed communication quality data. The machine learning model is trained on historical path performance data including jitter, delay, and packet drop measurements. As the system operates, it collects actual path performance feedback and uses this to refine future path selections, creating a closed-loop system that progressively improves both selection efficiency and communication reliability
2Reliability
If multiple candidate paths are evaluated to ensure quality, then communication quality improves, but the complexity of path selection increases
Solution Approach 1:
The patent introduces a machine learning-based intermediary system that mediates between raw candidate path data and final path selection decisions. This intermediary model processes and evaluates multiple candidate paths using trained algorithms, translating complex path characteristics into quality predictions. By inserting this intelligent intermediary layer, the system can comprehensively evaluate multiple candidate paths for quality assurance while the ML model manages the computational complexity, preventing the complexity from directly impacting the core path selection mechanism
Solution Approach 2:
The system transforms the path selection problem by changing evaluation parameters from direct performance measurement to predictive quality scoring. Instead of measuring actual communication quality (which requires establishing connections), the machine learning model uses parameter transformations to predict path quality based on static candidate path information and historical data. This parameter change allows comprehensive quality evaluation of multiple paths without the complexity of actually testing each path, as the model infers quality from correlated parameters
Data Source
AI summary
A communication session is established between at least a first endpoint and a second endpoint, either or both of which is behind at least one network device in a network that performs network address translation. Candidate path information is obtained that indicates candidate paths in the network through which the communication session can traverse, taking into account, network address translation occurring in the network. The candidate path information is analyzed against training data and data about conditions observed on one or more candidate paths for the communication session with a machine learning-based interface selection process to produce path recommendation information indicating whether one or more candidate paths should or should not be used for the communication session between the first endpoint and the second endpoint. The path recommendation information is supplied to an endpoint in the communication session.