Machine Learning TCP Parameter Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern heterogeneous wireless networks face challenges in optimizing data delivery due to volatility and diversity, as existing techniques like compression and caching fail to address the dynamic and personalized nature of traffic, leading to inconsistent performance and high costs.
Innovation Solution
An adaptive network performance optimizer that incorporates expert knowledge into a machine learning framework to estimate and optimize TCP parameters, using adaptive learning datasets and supervised learning to dynamically adjust network settings based on real-time operating conditions and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compression or right-sizing content techniques are used, then data size is reduced, but network volatility and diversity issues remain unresolved
Solution Approach 1:
The patent changes network parameters dynamically by using machine learning models to predict optimal TCP parameter values (such as window size, timeout intervals, and congestion control thresholds) based on historical network conditions and current traffic patterns, allowing the system to adapt to volatility while maintaining reliable performance
Solution Approach 2:
The system implements feedback loops where network performance metrics are continuously monitored, fed into machine learning models, and used to adjust transmission parameters in real-time, creating a closed-loop control system that maintains reliability despite network variations
2Reliability
If TCP parameters are adjusted to improve data transfer reliability, then delivery consistency improves, but system complexity increases
Solution Approach 1:
The system employs self-service mechanisms where machine learning models automatically learn optimal parameter configurations from historical data and autonomously adjust TCP parameters without requiring manual intervention or complex configuration management, reducing operational complexity while maintaining reliability
Solution Approach 2:
The patent replaces manual or rule-based parameter adjustment mechanisms with machine learning-based automated systems that use statistical models and algorithms to determine optimal parameters, substituting complex mechanical configuration processes with intelligent computational approaches
3Adaptability or versatility
If machine learning frameworks are used to optimize network parameters, then adaptability to changing conditions improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models offline using extensive historical network data, creating ready-to-use parameter estimation models that can quickly adapt to new conditions without requiring extensive real-time computation, thus reducing processing time while maintaining adaptability
Solution Approach 2:
The patent implements dynamic parameter adjustment where the machine learning system continuously adapts to changing network conditions by updating its models with new data streams, allowing the system to maintain high adaptability while using incremental learning approaches that minimize computational overhead compared to complete retraining
Data Source
AI summary
Based on expert input comprising recommended parameter values, a machine learning framework is constrained to perform machine learning to estimate optimal parameter values for TCP parameters in specific regions of an output parameter space. Network traffic data associated with a plurality of data requests to one or more computer applications are collected, over a time block, based on sampled parameter values for the TCP parameters. The sampled parameter values are distributed within the specific regions of the output parameter space. The machine learning is used to estimate the optimal parameter values for the TCP parameters. The optimal parameter values, for the TCP parameters, are propagated to and used by user devices to make new data requests to the computer applications.