Adaptive TCP Parameter Optimization for Mobile Download Performance
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Solution Overview
Problem
Modern heterogeneous networks face challenges in optimizing data delivery due to volatility and diversity, as existing techniques like caching and compression fail to effectively address variable bandwidth, latency, and device-specific factors, leading to inconsistent performance.
Innovation Solution
An adaptive network performance optimizer generates adaptive learning datasets to estimate and optimize TCP parameters by analyzing past wireless network traffic data, using supervised learning and machine learning algorithms to adjust network settings dynamically based on real-time operating conditions and performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If TCP parameters are optimized for specific network conditions, then download performance improves, but the system complexity increases due to the need to monitor and adjust multiple parameters dynamically
Solution Approach 1:
The system employs machine learning algorithms that automatically monitor network conditions and adjust TCP parameters without manual intervention. The algorithm learns from historical data and autonomously optimizes parameters like window size, timeout values, and congestion control settings, allowing the system to self-adjust to changing network conditions while maintaining improved download performance.
Solution Approach 2:
The patent dynamically changes multiple TCP parameters based on real-time network conditions. The system monitors metrics such as bandwidth, latency, and packet loss, then adjusts parameters like maximum segment size, retransmission timeout, and congestion window accordingly. This dynamic parameter adjustment enables the system to adapt to volatile network conditions and maintain optimal download performance across diverse network environments.
2Productivity
If multiple TCP parameters are adjusted simultaneously, then download outcomes improve, but the difficulty of detecting and measuring optimal values increases
Solution Approach 1:
The system implements a feedback mechanism where download performance metrics are continuously monitored and fed back to the machine learning algorithm. The algorithm uses this feedback to learn which parameter combinations yield the best results under specific network conditions. By tracking metrics such as download speed, completion time, and error rates, the system automatically identifies optimal parameter settings without requiring manual experimentation or complex measurement procedures.
Solution Approach 2:
The patent pre-configures a comprehensive set of TCP parameters to be potentially adjusted, including window size, timeout values, and congestion control settings. Rather than determining optimal values through trial and error during operation, the system prepares multiple candidate parameter sets in advance and selects from these pre-prepared options based on real-time network conditions, simplifying the detection and measurement process.
3Productivity
If compression and right-sizing techniques are used, then data transfer efficiency improves, but they fail to address fundamental network volatility issues
Solution Approach 1:
The patent combines compression techniques with dynamic TCP parameter adjustment to address both data transfer efficiency and network volatility. While compression reduces the amount of data to be transmitted, the system simultaneously monitors network conditions and adjusts TCP parameters to optimize the transmission process. This merged approach ensures that even when network conditions deteriorate, the system can adapt its transmission strategy to maintain reliable delivery, thereby addressing both efficiency and volatility resilience.
Data Source
AI summary
Network traffic data associated with data requests to computer applications based on static policies is collected. An optimization order is established among network parameters. A first network parameter of a higher rank in the optimization order is estimated based on the collected network traffic data before one or more other network parameters of lower ranks are estimated. Optimal values for the other network parameters are estimated based at least in part on the estimated first optimal value for the first network parameter. The estimated first optimal value of the first network parameter and the estimated optimal values for the other network parameters are propagated to be used by user devices to make new data requests to the computer applications.