User Device Server Selection for Dynamic IP Network Changes
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Solution Overview
Problem
The challenge of efficiently determining a routing path to an automatic configuration server that meets performance requirements in a network with dynamic IP version and address changes, leading to delays and resource waste in user device connections.
Innovation Solution
A mechanism where user devices collect connection information and transmit it to a server, which uses machine learning to determine candidate servers based on this information, allowing the user device to select an appropriate server for connection, adapting to network dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static configuration of IP version and IP address is used for user device, then configuration simplicity is maintained, but connection response time increases significantly due to network dynamic changes
Solution Approach 1:
The patent implements dynamic server selection by having the user device periodically collect connection information from multiple servers and use machine learning models to determine candidate servers. The system transitions from static IP configuration to dynamic adaptive selection, where the user device can switch between servers based on real-time network conditions, thereby reducing connection response time while maintaining operational simplicity through automated decision-making.
2Reliability
If user device connects to multiple automatic configuration servers with different IP versions, then service availability is improved, but routing path determination complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the user device and multiple automatic configuration servers. The user device collects connection information from various servers and feeds it into the machine learning model, which processes the data and outputs candidate server recommendations. This intermediary abstracts the complexity of routing path determination from the user device, allowing it to maintain connections to multiple servers for high availability while avoiding direct complex routing decisions.
Solution Approach 2:
The system implements a feedback mechanism where the user device periodically collects connection information from servers and uses this feedback to train or update the machine learning model. The model continuously learns from actual connection performance data and improves its server selection accuracy over time, enabling the system to adapt to changing network conditions while keeping the user device's routing determination simple through automated learning.
3Measurement precision
If server directs user device to select appropriate server manually, then control precision is improved, but system automation level decreases
Solution Approach 1:
The patent implements self-service by enabling the user device to autonomously collect connection information, process it through machine learning models, and automatically select candidate servers without manual intervention. The system performs server selection automation while maintaining high accuracy through the machine learning model's precise evaluation of connection parameters, thereby achieving both automation and precision simultaneously.
Data Source
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AI summary
The disclosure relates to a user device, servers, methods, apparatuses and a computer readable storage medium for network communication. There is provided a method implemented at a user device. The method includes: transmitting first connection information that the user device is connected to a plurality of servers to a first server, and receiving a list of candidate servers from the first server, the candidate servers being determined by the first server at least based on the first connection information. The method further includes selecting a second server from the list to establish a connection.